Startseite Long term pronostic value of suPAR in chronic heart failure: reclassification of patients with low MAGGIC score
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Long term pronostic value of suPAR in chronic heart failure: reclassification of patients with low MAGGIC score

  • Anne Marie Dupuy , Nils Kuster , Anne Sophie Bargnoux , Sylvain Aguilhon , Fabien Huet , Florence Leclercq , Jean-Luc Pasquié , François Roubille und Jean Paul Cristol EMAIL logo
Veröffentlicht/Copyright: 4. Februar 2021
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Clinical Chemistry and Laboratory Medicine (CCLM)
Aus der Zeitschrift Clinical Chemistry and Laboratory Medicine (CCLM) Band 59 Heft 7

Abstract

Objectives

Inflammation is a hallmark of heart failure (HF) and among inflammatory biomarkers, the most studied remains the C-reactive protein (CRP). In recent years several biomarkers have emerged, such as sST2 and soluble urokinase–type plasminogen activator receptor (suPAR). This study set out to examine the relative importance of long-time prognostic strength of suPAR and the potential additive information on patient risk with chronic HF in comparison with pronostic value of CRP and sST2.

Methods

Demographics, clinical and biological variables were assessed in a total of 182 patients with chronic HF over median follow-up period of 80 months. Inflammatory biomarkers (i.e., CRP, sST2, and suPAR) were performed.

Results

In univariate Cox regression analysis age, NYHA class, MAGGIC score and the five biomarkers (N-terminal pro brain natriuretic peptide [NT-proBNP], high-sensitive cardiac troponin T [hs-cTnT], CRP, sST2, and suPAR) were associated with both all-cause and cardiovascular mortality. In the multivariate model, only NT-proBNP, suPAR, and MAGGIC score remained independent predictors of all-cause mortality as well as of cardiovascular mortality. Risk classification analysis was significantly improved with the addition of suPAR particularly for all-cause short- and long-term mortality. Using a classification tree approach, the same three variables could be considered as significant classifier variables to predict all-cause or cardiovascular mortality and an algorithm were reported. We demonstrated the favorable outcome associated with patients with a low MAGGIC score and a low suPAR level by comparison to patients with low MAGGIC score but high suPAR values.

Conclusions

The main findings of our study are (1) that among the three inflammatory biomarkers, only suPAR levels were independently associated with 96-month mortality for patients with chronic HF and (2) that an algorithm based on clinical score, a cardiomyocyte stress biomarker and an inflammatory biomarker could help to a more reliable long term risk stratification in heart failure.

Keywords: biomarkers; heart failure; low grade inflammation; prognosis; soluble urokinase–type plasminogen activator receptor (suPAR)
Corresponding author: Jean Paul Cristol, Department of Biochemistry, Centre Ressources Biologiques de Montpellier, University Hospital of Montpellier, Montpellier cédex 534295, France; and PhyMedExp, University of Montpellier, INSERM U1046, CNRS UMR 9214, Montpellier, France, Fax: +33 4 67 33 83 93, E-mail:

Acknowledgments

The suPAR Assay was provided by Virogates, Birkerød, Danemark.

  1. Research funding: None declared.

  2. Author contributions: All authors have accepted responsibility for the entire content of this manuscript and approved its submission.

  3. Competing interests: Authors state no conflict of interest.

  4. Informed consent: Informed consent was obtained from all individuals included in this study.

  5. Ethical approval: The local Institutional Review Board deemed the study exempt from review.

References

1. Levine, B1, Kalman, J, Mayer, L, Fillit, HM, Packer, M. Elevated circulating levels of tumor necrosis factor in severe chronic heart failure. N Engl J Med 1990;323:236–41.10.1056/NEJM199007263230405Suche in Google Scholar PubMed

2. Yousuf, O, Mohanty, BD, Martin, SS, Joshi, PH, Blaha, MJ, Nasir, K, et al.. High-sensitivity C-reactive protein and cardiovascular disease: a resolute belief or an elusive link? J Am Coll Cardiol 2013;62:397–408.10.1016/j.jacc.2013.05.016Suche in Google Scholar PubMed

3. Li, Y, Zhong, X, Cheng, G, Zhao, C, Zhang, L, Hong, Y, et al.. Hs-CRP and all-cause, cardiovascular, and cancer mortality risk: a meta-analysis. Atherosclerosis 2017;259:75–82.10.1016/j.atherosclerosis.2017.02.003Suche in Google Scholar PubMed

4. Aimo, A, Vergaro, G, Passino, C, Ripoli, A, Ky, B, Miller, WL, et al.. Prognostic value of soluble suppression of tumorigenicity-2 in chronic heart failure: a meta-analysis. JACC Heart Fail 2017;5:280–6.10.1016/j.jchf.2016.09.010Suche in Google Scholar PubMed

5. Kuster, N, Huet, F, Dupuy, AM, Akodad, M, Battistella, P, Agollo, A, et al.. Multimarker approach including CRP, sST2, and GDF-15 for prognostic stratification in stable heart failure. ESC Heart Fail J 2020;7:2230–39.10.1002/ehf2.12680Suche in Google Scholar PubMed PubMed Central

6. Eugen-Olsen, J, Andersen, O, Linneberg, A, Ladelund, S, Hansen, TW, Langkilde, A, et al.. Circulating soluble urokinase plasminogen activator receptor predicts cancer, cardiovascular disease, diabetes and mortality in the general population. J Intern Med 2010;268:296–308.10.1111/j.1365-2796.2010.02252.xSuche in Google Scholar PubMed

7. Thunø, M, Macho, B, Eugen-Olsen, J. suPAR: the molecular crystal ball. Dis Markers 2009;27:157–72.10.1155/2009/504294Suche in Google Scholar

8. Eapen, DJ, Manocha, P, Ghasemzadeh, N, Patel, RS, Al Kassem, H, Hammadah, M, et al.. Soluble urokinase plasminogen activator receptor level is an independent predictor of the presence and severity of coronary artery disease and of future adverse events. J Am Heart Assoc 2014;3:e001118.10.1161/JAHA.114.001118Suche in Google Scholar PubMed PubMed Central

9. Dupuy, AM, Curinier, C, Kuster, N, Huet, F, Leclercq, F, Davy, JM, et al.. Multi-marker strategy in heart failure: combination of ST2 and CRP predicts poor outcome. PLoS One 2016;11:e0157159.10.1371/journal.pone.0157159Suche in Google Scholar PubMed PubMed Central

10. Dupuy, AM, Kuster, N, Curinier, C, Huet, F, Plawecki, M, Solecki, K, et al.. Exploring collagen remodeling and regulation as prognosis biomarkers in stable heart failure. Clin Chim Acta 2019;490:167–71.10.1016/j.cca.2018.08.042Suche in Google Scholar PubMed

11. Sartipy, U, Dahlström, U, Edner, M, Lund, LH. Predicting survival in heart failure: validation of the MAGGIC heart failure risk score in 51,043 patients from the Swedish heart failure registry. Eur J Heart Fail 2014;16:173–79.10.1111/ejhf.32Suche in Google Scholar

12. Heagerty, PJ, Lumley, T, Pepe, MS. Time-dependent ROC curves for censored survival data and a diagnostic marker. Biometrics 2000;56:337–44.10.1111/j.0006-341X.2000.00337.xSuche in Google Scholar

13. Pencina, MJ, D’Agostino, RBS, Steyerberg, EW. Extensions of net reclassification improvement calculations to measure usefulness of new biomarkers. Stat Med 2011;15:11–21.10.1002/sim.4085Suche in Google Scholar

14. Strasser, H, Weber, C. On the asymptotic theory of permutation statistics. Math Methods Stat 1999;8:220–50.Suche in Google Scholar

15. Koller, L, Stojkovic, S, Richter, B, Sulzgruber, P, Potolidis, C, Liebhart, F, et al.. Soluble urokinase-type plasminogen activator receptor improves risk prediction in patients with chronic heart failure. JACC Heart Fail 2017;5:268–77.10.1016/j.jchf.2016.12.008Suche in Google Scholar

16. Emerging Risk Factors Collaboration, Kaptoge, S, Di Angelantonio, E, Lowe, G, Pepys, MB, Thompson, SG, et al.. C-reactive protein concentration and risk of coronary heart disease, stroke, and mortality: an individual participant meta-analysis. Lancet 2010;375:132–40.10.1016/S0140-6736(09)61717-7Suche in Google Scholar

17. Yamada, S, Gotoh, T, Nakashima, Y, Kayaba, K, Ishikawa, S, Nago, N, et al.. Distribution of serum C-reactive protein and its association with atherosclerotic risk factors in a Japanese population: Jichi Medical School Cohort Study. Am J Epidemiol 2001;153:1183–90.10.1093/aje/153.12.1183Suche in Google Scholar PubMed

18. Kelley-Hedgepeth, A, Lloyd-Jones, DM, Colvin, A, Matthews, KA, Johnston, J, Sowers, MR, et al.. Ethnic differences in C-reactive protein concentrations. Clin Chem 2008;54:1027–37.10.1373/clinchem.2007.098996Suche in Google Scholar PubMed

19. Dieplinger, B, Mueller, T. Soluble ST2 in heart failure. Clin Chim Acta 2015;443:57–70.10.1016/j.cca.2014.09.021Suche in Google Scholar PubMed

20. Kakkar, R, Lee, RT. The IL-33/ST2 pathway: therapeutic target and novel biomarker. Nat Rev 2008;7:827–40.10.1038/nrd2660Suche in Google Scholar PubMed PubMed Central

21. Tajima, S, Bando, M, Ohno, S, Sugiyama, Y, Oshikawa, K, Tominaga, S, et al.. ST2 gene induced by type 2 helper T cell (Th2) and proinflammatory cytokine stimuli may modulate lung injury and fibrosis. Exp Lung Res 2007;33:81–97.10.1080/01902140701198583Suche in Google Scholar PubMed

22. Oshikawa, K, Kuroiwa, K, Tago, K, Iwahana, H, Yanagisawa, K, Ohno, S, et al.. Elevated soluble ST2 protein levels in sera of patients with asthma with an acute exacerbation. Am J Respir Crit Care Med 2001;164:277–81.10.1164/ajrccm.164.2.2008120Suche in Google Scholar PubMed

23. Oshikawa, K, Yanagisawa, K, Ohno, S, Tominaga, S, Sugiyama, Y. Expression of ST2 in helper T lymphocytes of malignant pleural effusions. Am J Respir Crit Care Med 2002;165:1005–9.10.1164/ajrccm.165.7.2105109Suche in Google Scholar PubMed

24. Lyngbæ, S, Marott, JL, Sehestedt, T, Hansen, TW, Olsen, MH, Andersen, O, et al.. Cardiovascular risk prediction in the general population with use of suPAR, CRP, and Framingham Risk Score. Int J Cardiol 2013;167:2904–11.10.1016/j.ijcard.2012.07.018Suche in Google Scholar PubMed

25. Hamie, L, Daoud, G, Nemer, G, Nammour, T, El Chediak, A, Uthman, IW, et al.. SuPAR, an emerging biomarker in kidney and inflammatory diseases. Postgrad Med 2018;94:517–24.10.1136/postgradmedj-2018-135839Suche in Google Scholar PubMed

26. Hodges, GW, Bang, CN, Wachtell, K, Eugen-Olsen, J, Jeppesen, JL. suPAR: a new biomarker for cardiovascular disease? Can J Cardiol 2015;31:1293–302.10.1016/j.cjca.2015.03.023Suche in Google Scholar PubMed

27. Lyngbæk, S, Sehestedt, T, Marott, JL, Hansen, TW, Olsen, MH, Andersen, O, et al.. CRP and suPAR are differently related to anthropometry and subclinical organ damage. Int J Cardiol 2013;167:781–5.10.1016/j.ijcard.2012.03.040Suche in Google Scholar PubMed

28. Blasi, F, Carmeliet, P. uPAR: a versatile signalling orchestrator. Nat Rev Mol Cell Biol 2002;3:932–43.10.1038/nrm977Suche in Google Scholar PubMed

29. Madsen, CD, Ferraris, GM, Andolfo, A, Cunningham, O, Sidenius, N. uPAR-induced cell adhesion and migration: vitronectin provides the key. J Cell Biol 2007;177:927–39.10.1083/jcb.200612058Suche in Google Scholar PubMed PubMed Central

Supplementary Material

The online version of this article offers supplementary material https://doi.org/10.1515/cclm-2020-0903.

Received: 2020-06-12
Accepted: 2021-01-22
Published Online: 2021-02-04
Published in Print: 2021-06-25

© 2021 Walter de Gruyter GmbH, Berlin/Boston

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https://doi.org/10.1515/cclm-2020-0903
Schlagwörter für diesen Artikel
biomarkers; heart failure; low grade inflammation; prognosis; soluble urokinase–type plasminogen activator receptor (suPAR)

Artikel in diesem Heft

  1. Frontmatter
  2. Editorial
  3. Machine learning and coagulation testing: the next big thing in hemostasis investigations?
  4. Reviews
  5. Updates on liquid biopsy: current trends and future perspectives for clinical application in solid tumors
  6. The underestimated issue of non-reproducible cardiac troponin I and T results: case series and systematic review of the literature
  7. Opinion Paper
  8. Benefits, limitations and controversies on patient-based real-time quality control (PBRTQC) and the evidence behind the practice
  9. Genetics and Molecular Diagnostics
  10. ctDNA from body fluids is an adequate source for EGFR biomarker testing in advanced lung adenocarcinoma
  11. General Clinical Chemistry and Laboratory Medicine
  12. Incidence, characteristics and outcomes among inpatient, outpatient and emergency department with reported high critical serum potassium values
  13. Clinical usefulness of drug-laboratory test interaction alerts: a multicentre survey
  14. Integrating quality assurance in autoimmunity: the changing face of the automated ANA IIF test
  15. Plasma thiol/disulphide homeostasis changes in patients with restless legs syndrome
  16. Reference Values and Biological Variations
  17. High-resolution pediatric reference intervals for 15 biochemical analytes described using fractional polynomials
  18. Continuous reference intervals for leukocyte telomere length in children: the method matters
  19. Hematology and Coagulation
  20. Using machine learning to identify clotted specimens in coagulation testing
  21. Cardiovascular Diseases
  22. Long term pronostic value of suPAR in chronic heart failure: reclassification of patients with low MAGGIC score
  23. Infectious Diseases
  24. Monocyte distribution width (MDW) parameter as a sepsis indicator in intensive care units
  25. A low level of CD16pos monocytes in SARS-CoV-2 infected patients is a marker of severity
  26. Thrombin generation in patients with COVID-19 with and without thromboprophylaxis
  27. Corrigendum
  28. Applying the concept of uncertainty to the sFlt-1/PlGF cut-offs for diagnosis and prognosis of preeclampsia
  29. Letters to the Editors
  30. Additional approaches for identifying non-reproducible cardiac troponin results
  31. Paediatric reference intervals for ionised calcium – a data mining approach
  32. A case of interference in testosterone, DHEA-S and progesterone measurements by second generation immunoassays
  33. Lack of cross-reactivity between anti-A IgG isoagglutinins and anti-SARS-CoV-2 IgG antibodies
  34. Artefactual bands on urine protein immunofixation gels
  35. A case of methaemoglobinaemia interference on the WDF channel on Sysmex XN-Series analysers
  36. Soluble fms-like tyrosine kinase-1: a potential early predictor of respiratory failure in COVID-19 patients
  37. Serendipitous detection of α1-antitrypsin deficiency: a single institution’s experience over a 32 month period
  38. The activated partial thromboplastin time may not reveal even severe fibrinogen deficiency
  39. Influence of C-reactive protein on thrombin generation assay
  40. Inappropriate extrapolations abound in fecal microbiota research
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