Age-Biomarkers-Clinical Risk Factors for Prediction of Cardiovascular Events in Patients With Coronary Artery Disease

Objective— In patients with stable coronary artery disease, conventional risk factors provide limited incremental predictive value for cardiovascular events. We sought to investigate whether a panel of cardiometabolic biomarkers alone or combined with conventional risk factors would exhibit incremental value in the prediction of cardiovascular events. Approach and Results— In the discovery cohort, we measured serum adiponectin, A-FABP (adipocyte fatty acid–binding protein), lipocalin-2, FGF (fibroblast growth factor)-19 and 21, plasminogen activator inhibitor-1, and retinol-binding protein-4 in 1166 Chinese coronary artery disease patients. After a median follow-up of 35 months, 170 patients developed new-onset major adverse cardiovascular events (MACE). In the model with age ≥65 years and conventional risk factors, area under the curve for predicting MACE was 0.68. Addition of lipocalin-2 to the age-clinical risk factor model improved predictive accuracy (area under the curve=0.73). Area under the curve further increased to 0.75 when a combination of lipocalin-2, A-FABP, and FGF-19 was added to yield age-biomarkers-clinical risk factor model. The adjusted hazard ratio on MACEs for lipocalin-2, A-FABP, and FGF-19 levels above optimal cutoffs were 2.23 (95% CI, 1.62–3.08), 1.99 (95% CI, 1.43–2.76), and 1.65 (95% CI, 1.15–2.35), respectively. In the validation cohort of 1262 coronary artery disease patients with type 2 diabetes mellitus, the age-biomarkers-clinical risk factor model was confirmed to provide good discrimination and calibration over the conventional risk factor alone for prediction of MACE. Conclusions— A combination of the 3 biomarkers, lipocalin-2, A-FABP, and FGF-19, with clinical risk factors to yield the age-biomarkers-clinical risk factor model provides an optimal and validated prediction of new-onset MACE in patients with stable coronary artery disease.