AIMS: multimarker approaches for risk prediction in coronary artery disease have remained inconsistent. We assessed multiple biomarkers representing distinct pathophysiological pathways in relation to cardiovascular events in stable angina. METHODS AND RESULTS: we investigated 12 biomarkers reflecting inflammation [C-reactive protein, growth-differentiation factor (GDF)-15, neopterin], lipid metabolism (apolipoproteins AI, B100), renal function (cystatin C, serum creatinine), and cardiovascular function and remodelling [copeptin, C-terminal-pro-endothelin-1, mid-regional-pro-adrenomedullin (MR-proADM), mid-regional-pro-atrial natriuretic peptide (MR-proANP), N-terminal-pro-B-type natriuretic peptide (Nt-proBNP)] in 1781 stable angina patients in relation to non-fatal myocardial infarction and cardiovascular death (n = 137) over 3.6 years. Using Cox proportional hazards models and C-indices, the strongest association with outcome for log-transformed biomarkers in multivariable-adjusted analyses was observed for Nt-proBNP [hazard ratio (HR) for one standard deviation increase 1.65, 95% confidence interval (CI) 1.28-2.13, C-index 0.686], GDF-15 (HR 1.59, 95% CI 1.25-2.02, C-index 0.681), MR-proANP (HR 1.46, 95% CI 1.14-1.87, C-index 0.673), cystatin C (HR 1.39, 95% CI 1.10-1.75, C-index 0.671), and MR-proADM (HR 1.63, 95% CI 1.21-2.20, C-index 0.668). Each of these top single markers and their combination (C-index 0.690) added predictive information beyond the baseline model consisting of the classical risk factors assessed by C-index and led to substantial reclassification (P-integrated discrimination improvement <0.05). CONCLUSION: comparative analysis of 12 biomarkers revealed Nt-proBNP, GDF-15, MR-proANP, cystatin C, and MR-proADM as the strongest predictors of cardiovascular outcome in stable angina. All five biomarkers taken separately offered incremental predictive ability over established risk factors. Combination of the single markers slightly improved model fit but did not enhance risk prediction from a clinical perspective.
AIMS: multimarker approaches for risk prediction in coronary artery disease have remained inconsistent. We assessed multiple biomarkers representing distinct pathophysiological pathways in relation to cardiovascular events in stable angina. METHODS AND RESULTS: we investigated 12 biomarkers reflecting inflammation [C-reactive protein, growth-differentiation factor (GDF)-15, neopterin], lipid metabolism (apolipoproteins AI, B100), renal function (cystatin C, serum creatinine), and cardiovascular function and remodelling [copeptin, C-terminal-pro-endothelin-1, mid-regional-pro-adrenomedullin (MR-proADM), mid-regional-pro-atrial natriuretic peptide (MR-proANP), N-terminal-pro-B-type natriuretic peptide (Nt-proBNP)] in 1781 stable anginapatients in relation to non-fatal myocardial infarction and cardiovascular death (n = 137) over 3.6 years. Using Cox proportional hazards models and C-indices, the strongest association with outcome for log-transformed biomarkers in multivariable-adjusted analyses was observed for Nt-proBNP [hazard ratio (HR) for one standard deviation increase 1.65, 95% confidence interval (CI) 1.28-2.13, C-index 0.686], GDF-15 (HR 1.59, 95% CI 1.25-2.02, C-index 0.681), MR-proANP (HR 1.46, 95% CI 1.14-1.87, C-index 0.673), cystatin C (HR 1.39, 95% CI 1.10-1.75, C-index 0.671), and MR-proADM (HR 1.63, 95% CI 1.21-2.20, C-index 0.668). Each of these top single markers and their combination (C-index 0.690) added predictive information beyond the baseline model consisting of the classical risk factors assessed by C-index and led to substantial reclassification (P-integrated discrimination improvement <0.05). CONCLUSION: comparative analysis of 12 biomarkers revealed Nt-proBNP, GDF-15, MR-proANP, cystatin C, and MR-proADM as the strongest predictors of cardiovascular outcome in stable angina. All five biomarkers taken separately offered incremental predictive ability over established risk factors. Combination of the single markers slightly improved model fit but did not enhance risk prediction from a clinical perspective.
Authors: Constantin von zur Muhlen; Eric Schiffer; Christine Sackmann; Petra Zürbig; Irene Neudorfer; Andreas Zirlik; Nay Htun; Alexander Iphöfer; Lothar Jänsch; Harald Mischak; Christoph Bode; Yung C Chen; Karlheinz Peter Journal: Mol Cell Proteomics Date: 2012-02-27 Impact factor: 5.911
Authors: Marc S Sabatine; David A Morrow; James A de Lemos; Torbjorn Omland; Sarah Sloan; Petr Jarolim; Scott D Solomon; Marc A Pfeffer; Eugene Braunwald Journal: Circulation Date: 2011-12-16 Impact factor: 29.690
Authors: Wouter J Kikkert; Bimmer E Claessen; Gregg W Stone; Roxana Mehran; Bernhard Witzenbichler; Bruce R Brodie; Jochen Wöhrle; Adam Witkowski; Giulio Guagliumi; Krzysztof Zmudka; José P S Henriques; Jan G P Tijssen; Elias A Sanidas; Vasiliki Chantziara; Ke Xu; George D Dangas Journal: J Thromb Thrombolysis Date: 2013-02 Impact factor: 2.300
Authors: Farzin Beygui; Philipp S Wild; Tanja Zeller; Marine Germain; Raphaele Castagné; Karl J Lackner; Thomas Münzel; Gilles Montalescot; Gary F Mitchell; Germaine C Verwoert; Kirill V Tarasov; David-Alexandre Trégouët; François Cambien; Stefan Blankenberg; Laurence Tiret Journal: Circ Cardiovasc Genet Date: 2014-07-22
Authors: Christoph Sinning; Till Keller; Tanja Zeller; Francisco Ojeda; Michael Schlüter; Renate Schnabel; Edith Lubos; Christoph Bickel; Karl J Lackner; Patrick Diemert; Thomas Munzel; Stefan Blankenberg; Philipp S Wild Journal: Clin Res Cardiol Date: 2013-11-23 Impact factor: 5.460
Authors: Philipp J Hohensinner; Alexander Niessner; Kurt Huber; Cornelia M Weyand; Johann Wojta Journal: Curr Opin Infect Dis Date: 2011-06 Impact factor: 4.915