Abstract

To investigate the diagnostic value of plasma growth and differentiation factor-15 (GDF-15) level, GDF-15 mRNA expression in circulating mononuclear cells (MNCs), and plasma pro-B-type natriuretic peptide (NT-proBNP) level for heart failure in patients with different underlying cardiac diseases, namely dilated cardiomyopathy (DCM) and coronary artery heart disease (CAD), and assess their value in predicting the severity of heart failure and long-term cardiovascular disease (CVD) events. Fasting venous blood samples were collected from 261 patients with DCM and 251 patients with CAD admitted in our hospital between January, 2018 and January, 2019, with 132 healthy individuals serving as the control group. The plasma level of GDF-15 was measured by enzyme-linked immunosorbent assay (ELISA), and the expression of GDF-15 mRNA in the MNCs was measured by real-time PCR. We also analyzed the expression of GDF-15 in patients with different NYHA classes, and the ROC curve was used to evaluate the predictive power of GDF-15 mRNA for CVD events. The plasma levels of GDF-15 and GDF-15 mRNA in the MNCs were significantly higher in patients with DCM and CAD than in the control group (P < 0.01). Plasma GDF-15 levels were significantly higher in NYHA class Ⅳ patients than in class Ⅱ and Ⅲ patients, and GDF-15 mRNA expressions in the MNCs were much higher in class Ⅲ and Ⅳ patients than class Ⅱ patients (P < 0.01). ROC curve analysis showed that for predicting CVD events, the area under the curve (AUC) was 0.73 (95% CI: 0.69-0.77, P < 0.001) for NT-proBNP alone, as compared with 0.83 (95% CI: 0.79-0.86, P < 0.001) for GDF-15 mRNA in the MNCs combined with NT-proBNP. Plasma GDF-15 level and GDF-15 mRNA expression level in the MNSc can both be used as biomarkers for heart failure. Plasma level of GDF-15 is more sensitive for predicting NYHA class Ⅳ patients with heart failure, while GDF-15 mRNA level in the MNCs better predicts class Ⅱ patients. The combination of NT-proBNP with GDF-15 mRNAlevel in the MNCs can more accurately predict the risk of long-term CVD events.

Full Text
Paper version not known

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call

Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.