Increased levels of sST2 in patients with mitral annulus disjunction and ventricular arrhythmias

二尖瓣环分离和室性心律失常患者的 sST2 水平升高

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作者:Esther Scheirlynck, Lars A Dejgaard, Eystein Skjølsvik, Oyvind H Lie, Andreea Motoc, Einar Hopp, Kaoru Tanaka, T Ueland, Margareth Ribe, Carlos Collet, Thor Edvardsen, Steven Droogmans, Bernard Cosyns, Kristina H Haugaa

Conclusion

Circulating sST2 levels were higher in patients with MAD and ventricular arrhythmias compared with arrhythmia-free patients. Combining sST2, LVEF and LGE assessment improved risk stratification in patients with MAD.

Methods

We included patients with ≥1 mm MAD on cardiac MRI. We assessed left ventricular ejection fraction (LVEF) and fibrosis by late gadolinium enhancement (LGE). The occurrence of ventricular arrhythmia, defined as aborted cardiac arrest, sustained or non-sustained ventricular tachycardia, was retrospectively assessed. We assessed circulating sST2 and TGFβ1 levels.

Objective

Displacement of the mitral valve, mitral annulus disjunction (MAD), is described as a possible aetiology of sudden cardiac death. Stress-induced fibrosis in the mitral valve apparatus has been suggested as the underlying mechanism. We aimed to explore the association between stretch-related and fibrosis-related biomarkers and ventricular arrhythmias in MAD. We hypothesised that soluble suppression of tumourigenicity-2 (sST2) and transforming growth factor-β1 (TGFβ1) are markers of ventricular arrhythmias in patients with MAD.

Results

We included 72 patients with MAD, of which 22 (31%) had ventricular arrhythmias. Patients with ventricular arrhythmias had lower LVEF (60 % (±6) vs 63% (±6), p = 0.04), more frequently papillary muscle fibrosis (14 (64%) vs 10 (20%), p < 0.001) and higher sST2 levels (31.6 ± 10.1 ng/mL vs 25.3 ± 9.2 ng/mL, p = 0.01) compared with those without, while TGFβ1 levels did not differ (p = 0.29). Combining sST2 level, LVEF and papillary muscle fibrosis optimally detected individuals with arrhythmia (area under the curve 0.82, 95% CI 0.73 to 0.92) and improved the risk model (p < 0.05) compared with single parameters.

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