Utility of Serum Biomarkers of Myocardial Fibrosis in High-Gradient Severe Aortic Stenosis: An Explorative Cardiovascular Magnetic Resonance Imaging-Based Study

Abstract

Background: Myocardial fibrosis in aortic stenosis (AS) is associated with a significant risk of poor clinical outcomes. Myocardial fibrosis can be evaluated using cardiovascular magnetic resonance (CMR) imaging and may be useful for risk-stratifying patients at high risk for poorer outcomes. A circulating biomarker of fibrosis may be a cheaper, more accessible alternative to CMR in lower-to-middle-income countries. This study evaluated the correlation between serum biomarkers of myocardial fibrosis (TGF-β1, PICP, and PIIINP) with CMR markers of myocardial fibrosis (T1 mapping, extracellular volume fraction (ECV), and late gadolinium enhancement (LGE)). Methods: Twenty-one high-gradient (mean gradient ≥ 40 mmHg) severe AS (aortic valve area < 1.0 cm2) participants underwent T1 mapping and LGE imaging using CMR. Blood serum was collected for enzyme-linked immunosorbent assays of the listed biomarkers. Results: Serum TGF-β1 was associated significantly with the global T1 relaxation time on CMR (r = 0.46 with 95% CI 0.03 to 0.74, p = 0.04). In the high T1 time group (1056 vs. 1023 ms), trends toward elevated serum TGF-β1 concentration (13,044 vs. 10,341 pg/mL, p = 0.08) and ECV (26% vs. 24%, p = 0.07) were observed. The high T1 and trend towards elevated TGF-β1 concentration in this group tracked adverse LV remodeling and systolic dysfunction. There were no significant associations between PICP/PIIINP and T1 mapping or between the biomarkers and LGE quantity. Conclusions: Serum TGF-β1 is a potential surrogate for diffuse interstitial fibrosis measured by T1 mapping and ECV on CMR. Serum PICP and PIIINP may be less appropriate as surrogate markers of fibrosis in view of their temporal trends over the course of AS. Larger studies are needed to validate the utility of TGF-β1 as a marker of diffuse fibrosis and to evaluate the utility of serial PICP/PIIINP measurements to predict decompensation.