Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
Meta-Analysis
. 2020 May;13(5):e006749.
doi: 10.1161/CIRCHEARTFAILURE.119.006749. Epub 2020 May 15.

Aptamer-Based Proteomic Platform Identifies Novel Protein Predictors of Incident Heart Failure and Echocardiographic Traits

Matthew Nayor  1   2 , Meghan I Short  3   4 , Humaira Rasheed  5   6 , Honghuang Lin  7 , Christian Jonasson  5 , Qiong Yang  3 , Kristian Hveem  5 , Janine F Felix  8 , Alanna C Morrison  9 , Philipp S Wild  10 , Michael P Morley  11 , Thomas P Cappola  11   12 , Mark D Benson  13 ; CHARGE-Heart Failure Working Group; CHARGE-EchoGen Consortium; Debby Ngo  14 , Sumita Sinha  13 , Michelle J Keyes  13 , Dongxiao Shen  13 , Thomas J Wang  15 , Martin G Larson  1   3 , Ben M Brumpton  5   6   16 , Robert E Gerszten  13 , Torbjørn Omland  17 , Ramachandran S Vasan  1   18
Affiliations
Meta-Analysis

Aptamer-Based Proteomic Platform Identifies Novel Protein Predictors of Incident Heart Failure and Echocardiographic Traits

Matthew Nayor et al. Circ Heart Fail. 2020 May.

Erratum in

Abstract

Background: We used a large-scale, high-throughput DNA aptamer-based discovery proteomic platform to identify circulating biomarkers of cardiac remodeling and incident heart failure (HF) in community-dwelling individuals.

Methods: We evaluated 1895 FHS (Framingham Heart Study) participants (age 55±10 years, 54% women) who underwent proteomic profiling and echocardiography. Plasma levels of 1305 proteins were related to echocardiographic traits and to incident HF using multivariable regression. Statistically significant protein-HF associations were replicated in the HUNT (Nord-Trøndelag Health) study (n=2497, age 63±10 years, 43% women), and results were meta-analyzed. Genetic variants associated with circulating protein levels (pQTLs) were related to echocardiographic traits in the EchoGen (n=30 201) and to incident HF in the CHARGE (n=20 926) consortia.

Results: Seventeen proteins associated with echocardiographic traits in cross-sectional analyses (false discovery rate <0.10), and 8 of these proteins had pQTLs associated with echocardiographic traits in EchoGen (P<0.0007). In Cox models adjusted for clinical risk factors, 29 proteins demonstrated associations with incident HF in FHS (174 HF events, mean follow-up 19 [limits, 0.2-23.7] years). In meta-analyses of FHS and HUNT, 6 of these proteins were associated with incident HF (P<3.8×10-5; 3 with higher risk: NT-proBNP [N-terminal proB-type natriuretic peptide], TSP2 [thrombospondin-2], MBL [mannose-binding lectin]; and 3 with lower risk: ErbB1 [epidermal growth factor receptor], GDF-11/8 [growth differentiation factor-11/8], and RGMC [hemojuvelin]). For 5 of the 6 proteins, pQTLs were associated with echocardiographic traits (P<0.0006) in EchoGen, and for RGMC, a protein quantitative trait loci was associated with incident HF (P=0.001).

Conclusions: A large-scale proteomics approach identified new predictors of cardiac remodeling and incident HF. Future studies are warranted to elucidate how biological pathways represented by these proteins may mediate cardiac remodeling and HF risk and to assess if these proteins can improve HF risk prediction.

Keywords: echocardiography; epidemiology; heart failure; proteomics; risk factors.

PubMed Disclaimer

Figures

Figure 1.
Figure 1.. Flowchart of the analytical approach
Abbreviations: MV, multivariable; pQTLs, protein quantitative trait loci; HF, heart failure; FHS, Framingham Heart Study; HUNT, Nord-Trøndelag Health Study; CHARGE, Cohorts for Heart and Aging Research in Genomic Epidemiology; MAGNet, Myocardial Applied Genomics Network
Figure 2.
Figure 2.. Multivariable-adjusted Associations of Proteins with Incident Heart Failure.
A, Proteins associated with higher risk of HF. B, Proteins associated with lower risk of HF Abbreviations: NT-proBNP, N-terminal proB-type natriuretic peptide; ErbB1, epidermal growth factor receptor; MBL, mannose binding lectin; RGMC, repulsive guidance molecule C (hemojuvelin); GDF-11/8, growth/differential factor 11/8; TSP2, thrombospondin-2; NRP1, neuropilin 1; vWF, von Willebrand factor; Apo E4, apolipoprotein E (isoform E4); Apo E3, apolipoprotein E (isoform E3); RGMA, repulsive guidance molecule A; Mn SOD, superoxide dismutase [Mn], mitochondrial; Notch 1, neurogenic locus notch homolog protein 1; RGMB, RGM domain family member B; IL-1Ra, interleukin 1 receptor antagonist; NEGR1, neuronal growth regulator 1; Apo L1, apolipoprotein L1; ISLR2, immunoglobulin superfamily containing leucine rich repeat 2; VEGF sR2, vascular endothelial growth factor receptor 2; S100A12, S100 calcium binding protein A12; NCAM-120, neural cell adhesion molecule 1, 120 kDa isoform; TNF-a, tumor necrosis factor alpha; MIC-1, macrophage inhibitor cytokine 1; DKK4, Dickkopf-related protein 4; IgG, immunoglobulin G; PEX5, peroxisomal biogenesis factor 5; C7, complement component 7; BMPR1A, bone morphogenetic protein receptor type 1A Hazard ratios represent the relative hazard for a 1 SD increment in the transformed and normalized protein level Multivariable model was adjusted for age, sex, body mass index, hypertension treatment, systolic blood pressure, total/HDL cholesterol, diabetes, smoking, prior myocardial infarction, and interim myocardial infarction * Denotes proteins meeting criteria for statistical significance in meta-analysis
Figure 2.
Figure 2.. Multivariable-adjusted Associations of Proteins with Incident Heart Failure.
A, Proteins associated with higher risk of HF. B, Proteins associated with lower risk of HF Abbreviations: NT-proBNP, N-terminal proB-type natriuretic peptide; ErbB1, epidermal growth factor receptor; MBL, mannose binding lectin; RGMC, repulsive guidance molecule C (hemojuvelin); GDF-11/8, growth/differential factor 11/8; TSP2, thrombospondin-2; NRP1, neuropilin 1; vWF, von Willebrand factor; Apo E4, apolipoprotein E (isoform E4); Apo E3, apolipoprotein E (isoform E3); RGMA, repulsive guidance molecule A; Mn SOD, superoxide dismutase [Mn], mitochondrial; Notch 1, neurogenic locus notch homolog protein 1; RGMB, RGM domain family member B; IL-1Ra, interleukin 1 receptor antagonist; NEGR1, neuronal growth regulator 1; Apo L1, apolipoprotein L1; ISLR2, immunoglobulin superfamily containing leucine rich repeat 2; VEGF sR2, vascular endothelial growth factor receptor 2; S100A12, S100 calcium binding protein A12; NCAM-120, neural cell adhesion molecule 1, 120 kDa isoform; TNF-a, tumor necrosis factor alpha; MIC-1, macrophage inhibitor cytokine 1; DKK4, Dickkopf-related protein 4; IgG, immunoglobulin G; PEX5, peroxisomal biogenesis factor 5; C7, complement component 7; BMPR1A, bone morphogenetic protein receptor type 1A Hazard ratios represent the relative hazard for a 1 SD increment in the transformed and normalized protein level Multivariable model was adjusted for age, sex, body mass index, hypertension treatment, systolic blood pressure, total/HDL cholesterol, diabetes, smoking, prior myocardial infarction, and interim myocardial infarction * Denotes proteins meeting criteria for statistical significance in meta-analysis
Figure 3.
Figure 3.. Correlations of 6 proteins related to incident HF in meta-analysis of FHS and HUNT
Age- and sex-adjusted partial correlations are displayed.
See this image and copyright information in PMC

References

    1. Ibrahim NE and Januzzi JL Jr. Established and Emerging Roles of Biomarkers in Heart Failure. Circ Res. 2018;123:614–629. - PubMed
    1. Felker GM and Ahmad T. Biomarkers for the Prevention of Heart Failure: Are We There Yet? J Am Coll Cardiol. 2018;72:3255–3258. - PubMed
    1. de Boer RA, Nayor M, deFilippi CR, Enserro D, Bhambhani V, Kizer JR, Blaha MJ, Brouwers FP, Cushman M, Lima JAC, Bahrami H, van der Harst P, Wang TJ, Gansevoort RT, Fox CS, Gaggin HK, Kop WJ, Liu K, Vasan RS, Psaty BM, Lee DS, Hillege HL, Bartz TM, Benjamin EJ, Chan C, Allison M, Gardin JM, Januzzi JL, Jr., Shah SJ, Levy D, Herrington DM, Larson MG, van Gilst WH, Gottdiener JS, Bertoni AG and Ho JE. Association of Cardiovascular Biomarkers With Incident Heart Failure With Preserved and Reduced Ejection Fraction. JAMA Cardiol. 2018;3:215–224. - PMC - PubMed
    1. Chow SL, Maisel AS, Anand I, Bozkurt B, de Boer RA, Felker GM, Fonarow GC, Greenberg B, Januzzi JL Jr., Kiernan MS, Liu PP, Wang TJ, Yancy CW, Zile MR, American Heart Association Clinical Pharmacology Committee of the Council on Clinical C, Council on Basic Cardiovascular S, Council on Cardiovascular Disease in the Y, Council on C, Stroke N, Council on Cardiopulmonary CCP, Resuscitation, Council on E, Prevention, Council on Functional G, Translational B, Council on Quality of C and Outcomes R. Role of Biomarkers for the Prevention, Assessment, and Management of Heart Failure: A Scientific Statement From the American Heart Association. Circulation. 2017;135:e1054–e1091. - PubMed
    1. Lemesle G, Maury F, Beseme O, Ovart L, Amouyel P, Lamblin N, de Groote P, Bauters C and Pinet F. Multimarker proteomic profiling for the prediction of cardiovascular mortality in patients with chronic heart failure. PLoS One. 2015;10:e0119265. - PMC - PubMed

Publication types

MeSH terms