Monitoring calcific aortic valve disease: the role of biomarkers

Clinical Relevance of Plasma Endogenous Tissue-Plasminogen Activator and Aortic Valve Sclerosis: Performance as a Diagnostic Biomarker

Background: Aortic valve sclerosis (AVSc), a common precursor to calcific aortic valve disease, may progress into advanced aortic stenosis with hemodynamic instability. However, plasma biomarkers of such a subclinical condition remain lacking. Since impaired fibrinolysis featuring dysregulated tissue plasminogen activator (t-PA) is involved in several cardiovascular diseases, we investigated whether endogenous t-PA was also associated with AVSc.

Methods: Plasma t-PA levels were measured in 295 consecutive patients undergoing standard echocardiography and Doppler flow imaging. Multiple logistic regression analyses were used to assess the association between t-PA and AVSc. Receiver operating characteristic curve analysis was performed for determining the diagnostic value of t-PA for AVSc. The performance of adding t-PA to clinical signatures of AVSc was evaluated. Concentration of t-PA was assessed in human sclerotic and non-sclerotic aortic valves by histology and immunohistochemistry analysis.

Results: Plasma t-PA was higher in patients with AVSc than in non-AVSc counterparts (median, 2063.10 vs. 1403.17 pg/mL, p < 0.01). C-statistics of plasma t-PA for discriminating AVSc was 0.698 (95%CI: 0.639–0.758). The performance of t-PA for identifying AVSc was better among male and non-hypertensive patients [C-statistics (95%CI): 0.712 (0.634–0.790) and 0.805 (0.693–0.916), respectively]. Combination of t-PA and clinical factors improved classification of the patients (category-free NRI: 0.452, p < 0.001; IDI: 0.020, p = 0.012). The concentration of t-PA was three times higher in sclerotic compared to non-sclerotic aortic valves.

Conclusion: Elevated circulating t-PA level confers an increased risk for AVSc. Further prospective studies with larger sample size are needed to examine if t-PA could serve as a diagnostic clinical marker for AVSc.

Biomarkers in Aortic Valve Stenosis and their Clinical Significance in Transcatheter Aortic Valve Implantation

Aortic valve stenosis is one of the most common valvular heart disorders and the prevalence will rise as the population ages. Once symptomatic patients with aortic valve stenosis tend to fare worse with high mortality rates. Aortic valve replacement is indicated in these patients and besides the standard surgical replacement, a less invasive approach, transcatheter aortic valve implantation, has gained momentum and has showed promising and solid results in patients with high surgical risk. An important aspect of evaluating patients with aortic valve stenosis is the ability to choose the best possible candidate for the procedure. In addition, predicting the short and long-term clinical outcomes after the valve replacement could offer the treating physicians a better insight and provide information for optimal therapy. Biomarkers are biological parameters that can be objectively measured and evaluated as indicators of normal biological processes and are easily monitored. The aim of this review is to critically assess some of the most widely used biomarkers at present (natriuretic peptides, troponins, C-reactive protein) and provide an insight in novel biomarkers that are currently being investigated (galectin-3, growth differentiation factor-15, microRNAs) for possible diagnostic and prognostic use in aortic valve stenosis and transcatheter aortic valve implantation respectively.

MicroRNA Expression Signature in Human Calcific Aortic Valve Disease

Altered microRNA (miRNA, miR) expression has been related to many disease processes; however, the miRNA expression signature in calcific aortic valve disease (CAVD) is unclear. In this study, microarrays were used to determine the miRNA expression signature of tissue samples from healthy individuals (n = 4) and patients with CAVD (n = 4). TargetScan, PITA, and microRNAorg 3-way databases were used to predict the potential target genes. DIANA-miRPath was used to incorporate the aberrant miRNAs into gene pathways. miRNA microarrays identified 92 differentially expressed miRNAs in CAVD tissues. The principal component analysis (PCA) of these samples and the unsupervised hierarchical clustering analysis based on the 92 aberrantly expressed miRNAs noted that miRNA expression could be categorized into two well-defined clusters that corresponded to healthy control and CAVD. Bioinformatic analysis showed the miRNA targets and potential molecular pathways. Collectively, our study reported the miRNA expression signature in CAVD and may provide potential therapeutic targets for CAVD.