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Novel multi-marker proteomics in phenotypically matched patients with ST-segment myocardial infarction: association with clinical outcomes

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Abstract

Early prediction of significant morbidity or mortality in patients with acute ST-segment elevation myocardial infarction (STEMI) represents an unmet clinical need. In phenotypically matched population of 139 STEMI patients (72 cases, 67 controls) treated with primary percutaneous coronary intervention, we explored associations between a 24-h relative change from baseline in the concentration of 91 novel biomarkers and the composite outcome of death, heart failure, or shock within 90 days. Additionally, we used random forest models to predict the 90-day outcomes. After adjustment for false discovery rate, the 90-day composite was significantly associated with concentration changes in 14 biomarkers involved in various pathophysiologic processes including: myocardial fibrosis/remodeling (collagen alpha-1, cathepsin Z, metalloproteinase inhibitor 4, protein tyrosine phosphatase subunits), inflammation, angiogenesis and signaling (interleukin 1 and 2 subunits, growth differentiation factor 15, galectin 4, trefoil factor 3), bone/mineral metabolism (osteoprotegerin, matrix extracellular phosphoglycoprotein and tartrate-resistant acid phosphatase), thrombosis (tissue factor pathway inhibitor) and cholesterol metabolism (LDL-receptor). Random forest models suggested an independent association when inflammatory markers are included in models predicting the outcomes within 90 days. Substantial heterogeneity is apparent in the early proteomic responses among patients with acutely reperfused STEMI patients who develop death, heart failure or shock within 90 days. These findings suggest the need to consider synergistic multi-biomarker strategies for risk stratification and to inform future development of novel post-myocardial infarction therapies.

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Data availability

The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.

Code availability

Assay characteristics including coefficients of variation and calibration are publicly available (www.olink.com/products/cvd-iii-panel). For all statistical analyses except the random forest model, SAS (version 9.4; SAS Institute, Cary, NC) was used; the package randomForest and R statistical software (version 3.5) were used for the random forest analysis.

References

  1. Velagaleti RS, Pencina MJ, Murabito JM et al (2008) Long-term trends in the incidence of heart failure after myocardial infarction. Circulation 118:2057–2062. https://doi.org/10.1161/CIRCULATIONAHA.108.784215

    Article  PubMed  PubMed Central  Google Scholar 

  2. Ezekowitz JA, Kaul P, Bakal JA et al (2009) Declining in-hospital mortality and increasing heart failure incidence in elderly patients with first myocardial infarction. J Am Coll Cardiol 53:13–20. https://doi.org/10.1016/j.jacc.2008.08.067

    Article  PubMed  Google Scholar 

  3. Granger CB, Goldberg RJ, Dabbous O et al (2003) Predictors of hospital mortality in the global registry of acute coronary events. Arch Intern Med 163:2345–2353. https://doi.org/10.1001/archinte.163.19.2345

    Article  PubMed  Google Scholar 

  4. Morrow DA, Antman EM, Charlesworth A et al (2000) TIMI risk score for ST-elevation myocardial infarction: a convenient, bedside, clinical score for risk assessment at presentation: an intravenous nPA for treatment of infarcting myocardium early II trial substudy. Circulation 102:2031–2037. https://doi.org/10.1161/01.CIR.102.17.2031

    Article  CAS  PubMed  Google Scholar 

  5. McAllister DA, Halbesma N, Carruthers K et al (2015) GRACE score predicts heart failure admission following acute coronary syndrome. Eur Heart J Acute Cardiovasc Care 4:165–171. https://doi.org/10.1177/2048872614542724

    Article  PubMed  Google Scholar 

  6. Truong QA, Cannon CP, Zakai NA et al (2009) Thrombolysis in myocardial infarction (TIMI) risk index predicts long-term mortality and heart failure in patients with ST-elevation myocardial infarction in the TIMI 2 clinical trial. Am Heart J 157:673–679. https://doi.org/10.1016/j.ahj.2008.12.010

    Article  PubMed  PubMed Central  Google Scholar 

  7. Vasan RS (2006) Biomarkers of cardiovascular disease: molecular basis and practical considerations. Circulation 113:2335–2362. https://doi.org/10.1161/CIRCULATIONAHA.104.482570

    Article  PubMed  Google Scholar 

  8. Grabowski M, Filipiak KJ, Malek LA et al (2007) Admission B-type natriuretic peptide assessment improves early risk stratification by Killip classes and TIMI risk score in patients with acute ST elevation myocardial infarction treated with primary angioplasty. Int J Cardiol 115:386–390. https://doi.org/10.1016/j.ijcard.2006.04.038

    Article  PubMed  Google Scholar 

  9. Klingenberg R, Aghlmandi S, Räber L et al (2018) Improved risk stratification of patients with acute coronary syndromes using a combination of hsTnT, NT-proBNP and hsCRP with the GRACE score. Eur Heart J Acute Cardiovasc Care 7:129–138. https://doi.org/10.1177/2048872616684678

    Article  PubMed  Google Scholar 

  10. van Diepen S, Newby LK, Lopes RD et al (2013) Prognostic relevance of baseline pro- and anti-inflammatory markers in STEMI: An APEX AMI substudy. Int J Cardiol 168:2127–2133. https://doi.org/10.1016/j.ijcard.2013.01.004

    Article  PubMed  Google Scholar 

  11. O’Donoghue ML, Morrow DA, Cannon CP et al (2016) Multimarker risk stratification in patients with acute myocardial infarction. J Am Heart Assoc 5:e002586. https://doi.org/10.1161/JAHA.115.002586

    Article  PubMed  PubMed Central  Google Scholar 

  12. Tromp J, Westenbrink BD, Ouwerkerk W et al (2018) Identifying pathophysiological mechanisms in heart failure with reduced versus preserved ejection fraction. J Am Coll Cardiol 72:1081–1090. https://doi.org/10.1016/j.jacc.2018.06.050

    Article  CAS  PubMed  Google Scholar 

  13. Armstrong PW, Adams PX, Al-Khalidi HR et al (2005) Assessment of pexelizumab in acute myocardial infarction (APEX AMI): a multicenter, randomized, double-blind, parallel-group, placebo-controlled study of pexelizumab in patients with acute myocardial infarction undergoing primary percutaneous coronary intervention. Am Heart J 149:402–407. https://doi.org/10.1016/j.ahj.2004.12.015

    Article  CAS  PubMed  Google Scholar 

  14. Armstrong PW, Bett N, Brieger D et al (2007) PexeLizumab for Acute ST-elevation myocardial infarction in patients undergoing primary percutaneous coronary intervention: a randomized controlled trial. JAMA 297:43–51. https://doi.org/10.1001/jama.297.1.43

    Article  CAS  PubMed  Google Scholar 

  15. Shavadia JS, Granger CB, Alemayehu W et al (2020) High-throughput targeted proteomics discovery approach and spontaneous reperfusion in ST-segment elevation myocardial infarction. Am Heart J 220:137–144. https://doi.org/10.1016/j.ahj.2019.09.015

    Article  CAS  PubMed  Google Scholar 

  16. Assarsson E, Lundberg M, Holmquist G et al (2014) Homogenous 96-plex PEA immunoassay exhibiting high sensitivity, specificity, and excellent scalability. PLoS ONE 9:e95192. https://doi.org/10.1371/journal.pone.0095192

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Lee EW, Wei LJ, Amato DA, Leurgans S (1992) Cox-type regression analysis for large numbers of small groups of correlated failure time observations. Survival analysis: state of the art. Springer, Dordrecht, pp 237–247

    Chapter  Google Scholar 

  18. Breiman L (2001) Random forests. Mach Learn 45:5–32. https://doi.org/10.1023/A:1010933404324

    Article  Google Scholar 

  19. Ferreira JP, Verdonschot J, Wang P et al (2021) Proteomic and mechanistic analysis of spironolactone in patients at risk for HF. JACC Heart Fail 9:268–277. https://doi.org/10.1016/j.jchf.2020.11.010

    Article  PubMed  Google Scholar 

  20. Prabhu SD, Frangogiannis NG (2016) The biological basis for cardiac repair after myocardial infarction. Circ Res 119:91–112. https://doi.org/10.1161/CIRCRESAHA.116.303577

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Fraccarollo D, Galuppo P, Bauersachs J, Ertl G (2002) Collagen accumulation after myocardial infarction: Effects of ETA receptor blockade and implications for early remodeling. Cardiovasc Res 54:559–567. https://doi.org/10.1016/S0008-6363(02)00256-0

    Article  CAS  PubMed  Google Scholar 

  22. Yin X, Subramanian S, Hwang SJ et al (2014) Protein biomarkers of new-onset cardiovascular disease: prospective study from the systems approach to biomarker research in cardiovascular disease initiative. Arterioscler Thromb Vasc Biol 34:939–945. https://doi.org/10.1161/ATVBAHA.113.302918

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Fraccarollo D, Galuppo P, Schraut S et al (2008) Immediate mineralocorticoid receptor blockade improves myocardial infarct healing by modulation of the inflammatory response. Hypertension 51:905–914. https://doi.org/10.1161/HYPERTENSIONAHA.107.100941

    Article  CAS  PubMed  Google Scholar 

  24. Murakami M, Kusachi S, Nakahama M et al (1998) Expression of the α1 and α2 chains of type IV collagen in the infarct zone of rat myocardial infarction. J Mol Cell Cardiol 30:1191–1202. https://doi.org/10.1006/jmcc.1998.0684

    Article  CAS  PubMed  Google Scholar 

  25. Querejeta R, López B, González A et al (2004) Increased collagen type I synthesis in patients with heart failure of hypertensive origin: relation to myocardial fibrosis. Circulation 110:1263–1268. https://doi.org/10.1161/01.CIR.0000140973.60992.9A

    Article  CAS  PubMed  Google Scholar 

  26. Burke RM, Lighthouse JK, Mickelsen DM, Small EM (2019) Sacubitril/valsartan decreases cardiac fibrosis in left ventricle pressure overload by restoring PKG signaling in cardiac fibroblasts. Circ Hear Fail 12:e005565. https://doi.org/10.1161/CIRCHEARTFAILURE.118.005565

    Article  CAS  Google Scholar 

  27. Bartko PE, Dal-Bianco JP, Guerrero JL et al (2017) Effect of losartan on mitral valve changes after myocardial infarction. J Am Coll Cardiol 70:1232–1244. https://doi.org/10.1016/j.jacc.2017.07.734

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Vidak E, Javoršek U, Vizovišek M, Turk B (2019) Cysteine cathepsins and their extracellular roles: shaping the microenvironment. Cells 8:264. https://doi.org/10.3390/cells8030264

    Article  CAS  PubMed Central  Google Scholar 

  29. Nguyen TD, Schwarzer M, Schrepper A et al (2018) Increased protein tyrosine phosphatase 1B (PTP1B) activity and cardiac insulin resistance precede mitochondrial and contractile dysfunction in pressure-overloaded hearts. J Am Heart Assoc 7:e008865. https://doi.org/10.1161/JAHA.118.008865

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Bouwens E, Brankovic M, Mouthaan H et al (2019) Temporal patterns of 14 blood biomarker candidates of cardiac remodeling in relation to prognosis of patients with chronic heart failure—the bio-SHiFT study. J Am Heart Assoc 8:e009555. https://doi.org/10.1161/JAHA.118.009555

    Article  PubMed  PubMed Central  Google Scholar 

  31. Liu CL, Guo J, Zhang X et al (2018) Cysteine protease cathepsins in cardiovascular disease: from basic research to clinical trials. Nat Rev Cardiol 15:351–370. https://doi.org/10.1038/s41569-018-0002-3

    Article  CAS  PubMed  Google Scholar 

  32. Buckley LF, Abbate A (2018) Interleukin-1 blockade in cardiovascular diseases: a clinical update. Eur Heart J 39:2063–2069. https://doi.org/10.1093/eurheartj/ehy128

    Article  CAS  PubMed  Google Scholar 

  33. Toldo S, Van Tassell BW, Abbate A (2017) Interleukin-1 blockade in acute myocardial infarction and heart failure: getting closer and closer. JACC 2:431–433. https://doi.org/10.1016/j.jacbts.2017.07.006

    Article  PubMed  PubMed Central  Google Scholar 

  34. Shang L, Feng M, Zhou X, Tang B (2019) Growth differentiation factor 15: a promising prognostic stratification biomarker in patients with acute coronary syndrome. Int J Cardiol 297:16. https://doi.org/10.1016/j.ijcard.2019.08.007

    Article  PubMed  Google Scholar 

  35. Qamar A, Giugliano RP, Bohula EA et al (2019) Biomarkers and clinical cardiovascular outcomes with ezetimibe in the IMPROVE-IT trial. J Am Coll Cardiol 74:1057–1068. https://doi.org/10.1016/j.jacc.2019.06.038

    Article  CAS  PubMed  Google Scholar 

  36. Bonaca MP, Morrow DA, Braunwald E et al (2011) Growth differentiation factor-15 and risk of recurrent events in patients stabilized after acute coronary syndrome: observations from PROVE IT-TIMI 22. Arterioscler Thromb Vasc Biol 31:203–210. https://doi.org/10.1161/ATVBAHA.110.213512

    Article  CAS  PubMed  Google Scholar 

  37. Stenemo M, Nowak C, Byberg L et al (2018) Circulating proteins as predictors of incident heart failure in the elderly. Eur J Heart Fail 20:55–62. https://doi.org/10.1002/ejhf.980

    Article  CAS  PubMed  Google Scholar 

  38. Gordon KJ, Blobe GC (2008) Role of transforming growth factor-β superfamily signaling pathways in human disease. Biochim Biophys Acta 1782:197–228. https://doi.org/10.1016/j.bbadis.2008.01.006

    Article  CAS  PubMed  Google Scholar 

  39. Meijers WC, Van Der Velde AR, Pascual-Figal DA, De Boer RA (2015) Galectin-3 and post-myocardial infarction cardiac remodeling. Eur J Pharmacol 763:115–121. https://doi.org/10.1016/j.ejphar.2015.06.025

    Article  CAS  PubMed  Google Scholar 

  40. Mosleh W, Chaudhari MR, Sonkawade S et al (2018) The therapeutic potential of blocking galectin-3 expression in acute myocardial infarction and mitigating inflammation of infarct region: a clinical outcome-based translational study. Biomark Insights 13:1177271918771969. https://doi.org/10.1177/1177271918771969

    Article  PubMed  PubMed Central  Google Scholar 

  41. Sharma UC, Mosleh W, Chaudhari MR et al (2017) Myocardial and serum galectin-3 expression dynamics marks post-myocardial infarction cardiac remodelling. Heart Lung Circ 26:736–745. https://doi.org/10.1016/j.hlc.2016.11.007

    Article  PubMed  Google Scholar 

  42. Erkol A, Oduncu V, Pala S et al (2012) Plasma osteoprotegerin level on admission is associated with no-reflow phenomenon after primary angioplasty and subsequent left ventricular remodeling in patients with acute ST-segment elevation myocardial infarction. Atherosclerosis 221:254–259. https://doi.org/10.1016/j.atherosclerosis.2011.12.031

    Article  CAS  PubMed  Google Scholar 

  43. Pedersen S, Mogelvang R, Bjerre M et al (2012) Osteoprotegerin predicts long-term outcome in patients with st-segment elevation myocardial infarction treated with primary percutaneous coronary intervention. Cardiol 123:31–38. https://doi.org/10.1159/000339880

    Article  CAS  Google Scholar 

  44. Andersen GØ, Knudsen EC, Aukrust P et al (2011) Elevated serum osteoprotegerin levels measured early after acute ST-elevation myocardial infarction predict final infarct size. Heart 97:460–465. https://doi.org/10.1136/hrt.2010.206714

    Article  CAS  PubMed  Google Scholar 

  45. Fuernau G, Zaehringer S, Eitel I et al (2013) Osteoprotegerin in ST-elevation myocardial infarction: prognostic impact and association with markers of myocardial damage by magnetic resonance imaging. Int J Cardiol 167:2134–2139. https://doi.org/10.1016/j.ijcard.2012.05.101

    Article  PubMed  Google Scholar 

  46. Ueland T, Dahl CP, Kjekshus J et al (2011) Osteoprotegerin predicts progression of chronic heart failure: results from CORONA. Circ Heart Fail 4:145–152. https://doi.org/10.1161/CIRCHEARTFAILURE.110.957332

    Article  CAS  PubMed  Google Scholar 

  47. Chirinos JA, Orlenko A, Zhao L et al (2020) Multiple plasma biomarkers for risk stratification in patients with heart failure and preserved ejection fraction. J Am Coll Cardiol 75:1281–1295. https://doi.org/10.1016/j.jacc.2019.12.069

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Røysland R, Bonaca MP, Omland T et al (2012) Osteoprotegerin and cardiovascular mortality in patients with non-ST elevation acute coronary syndromes. Heart 98:786–791. https://doi.org/10.1136/heartjnl-2011-301260

    Article  PubMed  Google Scholar 

  49. Ueland T, Yndestad A, Øie E et al (2005) Dysregulated osteoprotegerin/RANK ligand/RANK axis in clinical and experimental heart failure. Circulation 111:2461–2468. https://doi.org/10.1161/01.CIR.0000165119.62099.14

    Article  CAS  PubMed  Google Scholar 

  50. Bayes-Genis A, Núñez J, Zannad F et al (2017) The PCSK9-LDL receptor axis and outcomes in heart failure: BIOSTAT-CHF subanalysis. J Am Coll Cardiol 70:2128–2136. https://doi.org/10.1016/j.jacc.2017.08.057

    Article  CAS  PubMed  Google Scholar 

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Acknowledgements

The authors would like to thank Lisa Soulard of the Canadian VIGOUR Centre for her editorial assistance and preparation of the manuscript submission.

Funding

This work was supported by the Innovation and Investment Award Duke Clinical Research Institute, Canadian VIGOUR Center and Inova Heart Institute.

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Authors and Affiliations

  1. Canadian VIGOUR Centre, University of Alberta, Edmonton, AB, Canada

    Jay S. Shavadia, Wendimagegn Alemayehu, Cynthia M. Westerhout, Paul W. Armstrong & Sean van Diepen

  2. Division of Cardiology, Department of Medicine, University of Saskatchewan, Saskatoon, SK, Canada

    Jay S. Shavadia

  3. Inova Heart and Vascular Institute, Falls Church, VA, USA

    Christopher deFilippi

  4. Department of Cardiology, University of Groningen, Groningen, The Netherlands

    Jasper Tromp

  5. Duke Clinical Research Institute, Durham, NC, USA

    Christopher B. Granger

  6. Division of Cardiology, Department of Medicine, University of Alberta, Edmonton, AB, Canada

    Paul W. Armstrong & Sean van Diepen

  7. Department of Critical Care Medicine, University of Alberta, Edmonton, AB, Canada

    Sean van Diepen

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  1. Jay S. Shavadia
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  2. Wendimagegn Alemayehu
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  3. Christopher deFilippi
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  4. Cynthia M. Westerhout
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  6. Christopher B. Granger
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Correspondence to Jay S. Shavadia.

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Conflict of interest

Dr. deFilippi received grant support from Roche Diagnostics, Siemens Heathineers and has served as a consultant for Roche Diagnostics, Siemens Heathineers, Ortho Clinical, Abbott Diagnostics, Fiji Rebio, Metanomics, Quidel, UpToDate, and WebMD. Dr. Granger received grant support and consulting fees from Boehringer Ingelheim, Bayer, Bristol Myers Squibb, Daiichi Sankyo, Janssen Pharmaceutica, Pfizer, GlaxoSmithKline, and Sanofi, consulting fees and lecture fees from Boston Scientific, grant support from Merck, and consulting fees from AstraZeneca, Armetheon, Eli Lilly, Gilead, Hoffmann-La Roche, Medtronic, Takeda, and the Medicine Company. Dr. Povsic has received grants from Baxter Healthcare, Caladrius Biosciences, Capricor, CSL Behring, and Janssen Pharmaceuticals; and personal fees from Eli Lilly, NovoNordisk, and Pluristem. Dr. Armstrong has served as a consultant for Bayer and Merck, and received research grants from CSL, Boehringer Ingelheim, Bayer, and Merck. All other authors have no disclosures.

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Shavadia, J.S., Alemayehu, W., deFilippi, C. et al. Novel multi-marker proteomics in phenotypically matched patients with ST-segment myocardial infarction: association with clinical outcomes. J Thromb Thrombolysis 53, 841–850 (2022). https://doi.org/10.1007/s11239-021-02582-5

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