Growth differentiation factor-15 is associated with cardiovascular outcomes in patients with coronary artery disease.

BACKGROUND: Growth differentiation factor-15 (GDF-15) is a marker of inflammation, oxidative stress and it is associated with adverse prognosis in cardiovascular disease. The aim of the present cohort study is to investigate the prognostic value of GDF-15 in patients with coronary artery disease (CAD) during long-term follow up. METHODS: A total of 3641 consecutive patients with CAD were prospectively enrolled into the study and followed up for major adverse cardiovascular events (MACEs) and all-cause death up to 5.3-7.6 years. Plasma GDF-15 was measured and clinical data and long-term events were registered. The patients were subsequently divided into three groups by the levels of GDF-15 and the prognostic value of GDF-15 level with MACEs and all-cause death was evaluated. RESULTS: After a median follow-up at 6.4 years later, 775 patients (event rate of 21%) had developed MACEs and 275 patients died (event rate of 7.55%). Kaplan-Meier analysis indicated that the patients with GDF-15 > 1800 ng/L were significantly associated with an increased risk of MACEs and all-cause death. Cox regression analysis indicated that GDF-15 > 1800 ng/L were independently associated with the composite of MACEs (HR 1.74; 95% CI 1.44-2.02; P < 0.001) and all-cause death (HR 2.04; 95% CI 1.57-2.61; P < 0.001). For MACEs, GDF-15 significantly improved the C-statistic (area under the curve, 0.583 [95% CI 0.559-0.606] to 0.628 [0.605-0.651]; P < 0.001), net reclassification index (0.578; P = 0.031), and integrated discrimination index (0.021; P = 0.027). For all-cause death, GDF-15 significantly improved the C-statistic (0.728 [95% CI 0.694-0.761] to 0.817 [0.781-0.846]; P < 0.001), net reclassification index (0.629; P = 0.001), and integrated discrimination index (0.035; P = 0.002). CONCLUSIONS: In the setting of CAD, GDF-15 is associated with long-term MACEs and all-cause death, and provides incremental prognostic value beyond traditional risks factors.

Correlation Between Plasma Proteomics and Adverse Outcomes Among Older Men With Chronic Coronary Syndrome.

Background: Chronic coronary syndrome (CCS) is a newly proposed concept and is hallmarked by more long-term major adverse cardiovascular events (MACEs), calling for accurate prognostic biomarkers for initial risk stratification. Methods: Data-independent acquisition liquid chromatography tandem mass spectrometry (DIA LC-MS/MS) quantitative proteomics was performed on 38 patients with CCS; 19 in the CCS events group and 19 in the non-events group as the controls. We also developed a machine-learning-based pipeline to identify proteins as potential biomarkers and validated the target proteins by enzyme-linked immunosorbent assay in an independent prospective cohort. Results: Fifty-seven differentially expressed proteins were identified by quantitative proteomics and three final biomarkers were preliminarily selected from the machine-learning-based pipeline. Further validation with the prospective cohort showed that endothelial protein C receptor (EPCR) and cholesteryl ester transfer protein (CETP) levels at admission were significantly higher in the CCS events group than they were in the non-events group, whereas the carboxypeptidase B2 (CPB2) level was similar in the two groups. In the Cox survival analysis, EPCR and CETP were independent risk factors for MACEs. We constructed a new prognostic model by combining the Framingham coronary heart disease (CHD) risk model with EPCR and CETP levels. This new model significantly improved the C-statistics for MACE prediction compared with that of the Framingham CHD risk model alone. Conclusion: Plasma proteomics was used to find biomarkers of predicting MACEs in patients with CCS. EPCR and CETP were identified as promising prognostic biomarkers for CCS.