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
. 2021 Dec;23(12):2021-2032.
doi: 10.1002/ejhf.2361. Epub 2021 Oct 21.

Plasma biomarkers associated with adverse outcomes in patients with calcific aortic stenosis

Mahesh K Vidula  1 , Alena Orlenko  2 , Lei Zhao  3 , Lisa Salvador  3 , Aeron M Small  4 , Edward Horton  5 , Jordana B Cohen  6   7 , Srinath Adusumalli  1 , Srinivas Denduluri  1 , Taisei Kobayashi  1 , Matthew Hyman  1 , Paul Fiorilli  1 , Caroline Magro  2 , Bibi Singh  2 , Bianca Pourmussa  2 , Candy Greczylo  2 , Michael Basso  3 , Christina Ebert  3 , Melissa Yarde  3 , Zhuyin Li  3 , Mary Ellen Cvijic  3 , Zhaoqing Wang  3 , Alice Walsh  3 , Joseph Maranville  3 , Ellen Kick  3 , Joseph Luettgen  3 , Leonard Adam  3 , Peter Schafer  3 , Francisco Ramirez-Valle  3 , Dietmar Seiffert  3 , Jason H Moore  2 , David Gordon  3 , Julio A Chirinos  1   2
Affiliations
Free article

Plasma biomarkers associated with adverse outcomes in patients with calcific aortic stenosis

Mahesh K Vidula et al. Eur J Heart Fail. 2021 Dec.
Free article

Abstract

Aims: Enhanced risk stratification of patients with aortic stenosis (AS) is necessary to identify patients at high risk for adverse outcomes, and may allow for better management of patient subgroups at high risk of myocardial damage. The objective of this study was to identify plasma biomarkers and multimarker profiles associated with adverse outcomes in AS.

Methods and results: We studied 708 patients with calcific AS and measured 49 biomarkers using a Luminex platform. We studied the correlation between biomarkers and the risk of (i) death and (ii) death or heart failure-related hospital admission (DHFA). We also utilized machine-learning methods (a tree-based pipeline optimizer platform) to develop multimarker models associated with the risk of death and DHFA. In this cohort with a median follow-up of 2.8 years, multiple biomarkers were significantly predictive of death in analyses adjusted for clinical confounders, including tumour necrosis factor (TNF)-α [hazard ratio (HR) 1.28, P < 0.0001], TNF receptor 1 (TNFRSF1A; HR 1.38, P < 0.0001), fibroblast growth factor (FGF)-23 (HR 1.22, P < 0.0001), N-terminal pro B-type natriuretic peptide (NT-proBNP) (HR 1.58, P < 0.0001), matrix metalloproteinase-7 (HR 1.24, P = 0.0002), syndecan-1 (HR 1.27, P = 0.0002), suppression of tumorigenicity-2 (ST2) (IL1RL1; HR 1.22, P = 0.0002), interleukin (IL)-8 (CXCL8; HR 1.22, P = 0.0005), pentraxin (PTX)-3 (HR 1.17, P = 0.001), neutrophil gelatinase-associated lipocalin (LCN2; HR 1.18, P < 0.0001), osteoprotegerin (OPG) (TNFRSF11B; HR 1.26, P = 0.0002), and endostatin (COL18A1; HR 1.28, P = 0.0012). Several biomarkers were also significantly predictive of DHFA in adjusted analyses including FGF-23 (HR 1.36, P < 0.0001), TNF-α (HR 1.26, P < 0.0001), TNFR1 (HR 1.34, P < 0.0001), angiopoietin-2 (HR 1.26, P < 0.0001), syndecan-1 (HR 1.23, P = 0.0006), ST2 (HR 1.27, P < 0.0001), IL-8 (HR 1.18, P = 0.0009), PTX-3 (HR 1.18, P = 0.0002), OPG (HR 1.20, P = 0.0013), and NT-proBNP (HR 1.63, P < 0.0001). Machine-learning multimarker models were strongly associated with adverse outcomes (mean 1-year probability of death of 0%, 2%, and 60%; mean 1-year probability of DHFA of 0%, 4%, 97%; P < 0.0001). In these models, IL-6 (a biomarker of inflammation) and FGF-23 (a biomarker of calcification) emerged as the biomarkers of highest importance.

Conclusions: Plasma biomarkers are strongly associated with the risk of adverse outcomes in patients with AS. Biomarkers of inflammation and calcification were most strongly related to prognosis.

Keywords: Aortic stenosis; Biomarkers; Calcification; Inflammation; Machine learning.

PubMed Disclaimer

Comment in

References

    1. Otto CM, Nishimura RA, Bonow RO, Carabello BA, Erwin JP, Gentile F, Jneid H, Krieger EV, Mack M, McLeod C, O'Gara PT, Rigolin VH, Sundt TM, Thompson A, Toly C. 2020 ACC/AHA Guideline for the management of patients with valvular heart disease: a report of the American College of Cardiology/American Heart Association Joint Committee on Clinical Practice Guidelines. J Am Coll Cardiol 2021;77:450-500.
    1. Bonow RO. Asymptomatic aortic stenosis. J Am Coll Cardiol 2015;66:2839-2841.
    1. Chin CWL, Shah ASV, McAllister DA, Cowell SJ, Alam S, Langrish JP, Strachan FE, Hunter AL, Choy AM, Lang CC, Walker S, Boon NA, Newby DE, Mills NL, Dweck MR. High-sensitivity troponin I concentrations are a marker of an advanced hypertrophic response and adverse outcomes in patients with aortic stenosis. Eur Heart J 2014;35:2312-2321.
    1. Lindman BR, Breyley JG, Schilling JD, Vatterott AM, Zajarias A, Maniar HS, Damiano RJ, Moon MR, Lawton JS, Gage BF, Sintek MA, Aquino A, Holley CL, Patel NM, Lawler C, Lasala JM, Novak E. Prognostic utility of novel biomarkers of cardiovascular stress in patients with aortic stenosis undergoing valve replacement. Heart 2015;101:1382-1388.
    1. Bergler-Klein J, Klaar U, Heger M, Rosenhek R, Mundigler G, Gabriel H, Binder T, Pacher R, Maurer G, Baumgartner H. Natriuretic peptides predict symptom-free survival and postoperative outcome in severe aortic stenosis. Circulation 2004;109:2302-2308.

Publication types

LinkOut - more resources