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.