Elevated matrix metalloproteinase-2 (MMP-2) tissue levels have been associated with ascending thoracic aortic aneurysm (aTAA). As MMP-2 activation is controlled by interactions among matrix metalloproteinase-14 (MMP-14), a tissue inhibitor of metalloproteinases-2 (TIMP-2) and Pro-MMP-2 in cell culture, this activation process might also play a role in aTAA. Via gelatin zymography we analyzed tissue levels of MMP-2 isoforms (Pro-MMP-2, active MMP-2, total MMP-2) and via enzyme-linked immunosorbent assay (ELISA,) MMP-14,TIMP-2 and total MMP-2 tissue levels in N = 42 patients with aTAA. As controls, MMP-14 and TIMP-2 aortic tissue levels in N = 9 patients undergoing coronary artery bypass surgery were measured via ELISA, and levels of MMP-2 isoforms in N = 11 patients via gelatin zymography. Active MMP-2 was significantly higher in aTAA than in controls. Patients with aTAA exhibited significantly lower Pro-MMP-2 and TIMP-2 levels. Total MMP-2 and MMP-14 did not differ significantly between groups. Regression analysis revealed a linear relationship between TIMP-2 and the MMP-14/TIMP-2 ratio, as well as active MMP-2 in aTAA. Aneurysmatic tissue can be accurately distinguished from control aortic tissue (AUC = 1) by analyzing the active MMP-2/Pro-MMP-2 ratio with a cutoff value of 0.11, whereas MMP-14 and TIMP-2 roles are negligible in ROC analysis. A larger amount of MMP-2 is activated in aTAA than in control aortic tissue–a factor that seems to be a central process in aneurysm development. When active MMP-2 exceeds 10% compared to Pro-MMP-2, we conclude that it originates from aneurysmatic tissue, which we regard as a starting point for further studies of aTAA biomarkers. The tissue's MMP-14/TIMP-2 ratio may regulate the degree of Pro-MMP-2 activation as a determining factor, while the enzymatic activities of MMP-14 and TIMP-2 do not seem to play a key role in aneurysm development.

The pathogenesis of ascending thoracic aortic aneurysm (aTAA) is thought to differ between patients with bicuspid aortic valve (BAV) and tricuspid aortic valve (TAV), and one of the causes is different hemodynamics. Influenced by hemodynamics, the tissue levels of proteins associated with aTAA might differ between aTAAs with BAV and TAV and between different localities within the aortic wall. We therefore analyzed aTAA tissue levels of MMP-2 (matrix metalloproteinase-2) isoforms (Pro-MMP-2, active MMP-2, and total MMP-2) and tissue levels of MMP-14, TIMP-2 (tissue inhibitor of metalloproteinase-2), MMP-9, and TIMP-1 in 19 patients with BAV and 23 patients with TAV via gelatin zymography and enzyme-linked immunosorbent assay (ELISA), respectively. TAV and BAV groups’ protein levels did not differ significantly. Whereas the TAV group exhibited no significant differences in protein levels between the aneurysm’s anterior and posterior parts, the BAV group revealed significantly higher levels of Pro-MMP-2, total MMP-2, and TIMP-2 in the aneurysm’s posterior parts (mean Pro-MMP-2 200.52 arbitrary units (AU) versus 161.12 AU, p=0.007; mean total MMP-2 235.22 AU versus 193.68 AU, p=0.002; mean TIMP-2 26.90 ng/ml versus 25.36 ng/ml, p=0.009), whereas the other proteins did not differ significantly within the aortic wall. Thus, MMPs are distributed more heterogeneously within the aortic wall of aTAAs associated with BAV than in those associated with TAV, which is a new aspect for understanding the underlying pathogenesis. This heterogeneous protein level distribution might be attributable to differences in the underlying pathogenesis, especially hemodynamics. This result is important for further studies as it will be essential to specify the location of samples to ensure data comparability regarding the main goals of understanding the pathogenesis of aTAA, optimizing treatments, and establishing a screening method for its potentially deadly complications.

Background: To validate the automated quantification of cardiac chamber volumes and myocardial mass on non-contrast chest CT using cardiac MR (CMR) as a reference. Methods: We retrospectively included 53 consecutive patients who received non-contrast chest CT and CMR within three weeks. A deep learning model created cardiac segmentations on axial soft-tissue reconstructions from CT, covering all four cardiac chambers and the left ventricular myocardium. Segmentations on CMR cine short-axis and long-axis images served as a reference. Standard estimates of diagnostic accuracy were calculated for ventricular volumes at end-diastole and end-systole (LVEDV, LVESV, RVEDV, RVESV), left ventricular mass (LVM), and atrial volumes (LA, RA) at ventricular end-diastole. A qualitative assessment noted segmentation issues. Results: The deep learning model generated CT measurements for 52 of the 53 patients (98%). Based on CMR measurements, the average LVEDV was 166 ± 64 mL, RVEDV was 144 ± 51 mL, and LVM was 115 ± 39 g. The CT measurements correlated well with CMR measurements for LVEDV, LVESV, and LVM (ICC = 0.85, ICC = 0.84, and ICC = 0.91; all p < 0.001) and RVEDV and RVESV (ICC = 0.79 and ICC= 0.78; both p < 0.001), and moderately well with LA and RA (ICC = 0.74 and ICC = 0.61; both p < 0.001). Absolute agreements likewise favored LVEDV, LVM, and RVEDV. ECG-gating did not relevantly influence the results. The CT results correctly identified 7/15 LV and 1/1 RV as dilated (one and six false positives, respectively). Major qualitative issues were found in three cases (6%). Conclusions: Automated cardiac chamber volume and myocardial mass quantification on non-contrast chest CT produced viable measurements in this retrospective sample. Relevance Statement: An automated cardiac assessment on non-contrast chest CT provides quantitative morphological data on the heart, enabling a preliminary organ evaluation that aids in incidentally identifying at-risk patients who may benefit from a more targeted diagnostic workup.

Aims: To quantify greyzone fibrosis (GZF) in patients after acute myocardial infarction (MI) and to evaluate its correlation with MI-free survival and improvements in left ventricular ejection fraction (LVEF) compared with the established risk factors high-sensitivity cardiac troponin T (hs-cTnT) and Late Gadolinium Enhancement (LGE). Methods and results: The study involved 176 patients who experienced acute MI and underwent cardiac magnetic resonance (CMR) prior to hospital discharge, followed by a second CMR on average six months later. LGE was quantified in both examinations, a separate analysis of the GZF was conducted only in the follow-up CMR after resolution of the initial infarct edema. LVEF was measured in both CMR. hs-cTnT levels were assessed at hospital admission, as well as 8, 16, 24, 48 and 72 h after coronary intervention. Telephone follow-ups were conducted annually for up to 8 years. LGE measurements showed better correlation with MI-free survival (Harrell’s C of 0.711 of LGE mass) compared to GZF (0.579 of GZF mass). Additionally, hs-cTnT outperformed GZF (Harrell’s C of 0.645). As an univariable predictor for MI-free survival, only hs-cTnT reached significance (p < 0.05). With regard to improvements in ejection fraction, both hs-cTnT and LGE measurements showed acceptable correlation with improvement in ejection fraction (p < 0.05), while GZF measurements showed no correlation (p > 0.5). Conclusions: In CMR, the assessment of GZF demonstrated inferior p correlation compared to hs-cTnT and LGE in patients after acute MI with respect to the endpoint of MI-free survival. Furthermore, GZF showed no correlation with the improvement of LVEF. Graphical

Background/Objectives: The extent of myocardial scar, visualized by late gadolinium enhancement (LGE) on cardiac magnetic resonance imaging (CMR), is associated with mortality after acute myocardial infarction (MI). However, data on optimal cardiac biomarker cut-off values (e.g., high-sensitivity cardiac troponin T, hs-cTnT) for predicting LGE remain limited. This study aimed to evaluate the predictive value of cardiac biomarkers for LGE and their influence on clinical outcomes. Methods: We included 597 patients who underwent CMR a median of 3 days [interquartile range (IQR) 2–4 days] after MI (407 STEMI and 190 NSTEMI patients), with a median follow-up period of 3.0 years [IQR 1.3–3.5 years]. Results: After adjusting for key variables, maximum cardiac biomarker levels were found to have the strongest correlation with the presence and extent of LGE (p < 0.001). LGE mass and LVEF were the most robust predictors of all-cause mortality (hazard ratio [CI] 1.464 [1.050–2.040], p = 0.025, Harrell’s C 0.812; 0.697 [0.491–0.990], p = 0.044, Harrell’s C 0.810, respectively). We determined a receiver operating characteristic (ROC) area under the curve (AUC) of 0.73 and an optimal cut-off of 53 g for LGE mass and mortality, with a maximum hs-cTnT cut-off of 7270 ng/L predicting this extent of LGE. Conclusions: In this large cohort of MI patients with three-year follow-up, cardiac biomarker levels showed a strong correlation with the extent of LGE. While absolute LGE mass was associated with mortality, its predictive value was comparable to that of CMR-derived LVEF. These findings should be interpreted cautiously, given the study’s observational design, and should be considered hypothesis-generating, underscoring the need for prospective validation.

Objectives: The aim of this study was to evaluate the influence of three-dimensional transcatheter heart valve (THV) position on mid-term pacing burdens and to examine the effect of stimulation rates on survival after transcatheter aortic valve implantation (TAVI). Methods: In patients receiving a new PPM before discharge after TAVI, we analyzed the final three-dimensional THV position in 107 patients (78 with Sapien S3 THV and 29 with Evolut R THV) using fusion imaging from pre- and post-TAVI computed tomography angiography. The PPM stimulation rates were examined pre-discharge and after 3 months. Patient survival was tracked over a period of 5 years. Results: A high pacing burden (stimulation rate of >20%) was observed in 69% of patients before discharge and in 67% after three months. One-third of the patients with an initial low pacing burden developed a high pacing burden after 3 months. The implantation depth did not differ between patients with or without a high pacing burden at discharge (p = 0.550) or after 3 months (p = 0.901). The three-dimensional THV position showed no influence on the pacing burden pre-discharge (p = 0.550) and after 3 months (0.901). There were no significant differences in survival, regardless of the pacing burden pre-discharge and after 3 months (log-rank p = 0.676). Conclusions: Three-dimensional THV position did not predict a high pacing burden after TAVI. Pacing burden was not associated with survival. One-third of patients experienced an increase to a high pacing burden within 3 months, suggesting potential long-term conduction system damage after TAVI.