Multiple marker approach to risk stratification in patients with stable coronary artery disease

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. 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). 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.