BACKGROUND: Left ventricular (LV) reverse remodelling after valve replacement in aortic stenosis (AS) has been classically linked to the hydraulic performance of the replacement device, but myocardial status at the time of surgery has received little attention. OBJECTIVE: To establish predictors of LV mass (LVM) regression 1 year after valve replacement in a surgical cohort of patients with AS based on preoperative clinical and echocardiographic parameters and the myocardial gene expression profile at surgery. METHODS: Transcript levels of remodelling-related proteins and regulators were determined in LV intraoperative biopsies from 46 patients with AS by RT-PCR. Using multiple linear regression analysis, an equation was developed (adjusted R²=0.73; p<0.0001) that included positive [preoperative LVM, microRNA-133a, serum response factor (SRF, which is known to be a transactivator of miR-133) and age] and negative [body mass index (BMI), Wolf-Hirschhorn syndrome candidate-2 (WHSC2, which is a target for repression by miR-133a), β-myosin heavy chain, myosin light chain-2, diabetes mellitus, and male gender] independent predictors of LVM reduction. RESULTS: Aortic valve area gain or the reduction in transvalvular gradient maintained no significant relationships with the dependent variable. Logistic regression analysis identified microRNA-133a as a significant positive predictor of LVM normalisation, whereas β-myosin heavy chain and BMI constituted negative predictors. CONCLUSIONS: Hypertrophy regression 1 year after pressure overload release is related to the preoperative myocardial expression of remodelling-related genes, in conjunction with the patient's clinical background. In this scenario, miR-133 emerges as a key element of the reverse remodelling process. Postoperative improvement of valve haemodynamics does not predict the degree of hypertrophy regression or LVM normalisation. These results led us to reconsider the current reverse remodelling paradigm and (1) to include criteria of hypertrophy reversibility in the decision algorithm used to decide timing for the operation; and (2) to modify other prevailing factors (overweight, diabetes, etc) known to maintain LV hypertrophy.
BACKGROUND: Left ventricular (LV) reverse remodelling after valve replacement in aortic stenosis (AS) has been classically linked to the hydraulic performance of the replacement device, but myocardial status at the time of surgery has received little attention. OBJECTIVE: To establish predictors of LV mass (LVM) regression 1 year after valve replacement in a surgical cohort of patients with AS based on preoperative clinical and echocardiographic parameters and the myocardial gene expression profile at surgery. METHODS: Transcript levels of remodelling-related proteins and regulators were determined in LV intraoperative biopsies from 46 patients with AS by RT-PCR. Using multiple linear regression analysis, an equation was developed (adjusted R²=0.73; p<0.0001) that included positive [preoperative LVM, microRNA-133a, serum response factor (SRF, which is known to be a transactivator of miR-133) and age] and negative [body mass index (BMI), Wolf-Hirschhorn syndrome candidate-2 (WHSC2, which is a target for repression by miR-133a), β-myosin heavy chain, myosin light chain-2, diabetes mellitus, and male gender] independent predictors of LVM reduction. RESULTS: Aortic valve area gain or the reduction in transvalvular gradient maintained no significant relationships with the dependent variable. Logistic regression analysis identified microRNA-133a as a significant positive predictor of LVM normalisation, whereas β-myosin heavy chain and BMI constituted negative predictors. CONCLUSIONS:Hypertrophy regression 1 year after pressure overload release is related to the preoperative myocardial expression of remodelling-related genes, in conjunction with the patient's clinical background. In this scenario, miR-133 emerges as a key element of the reverse remodelling process. Postoperative improvement of valve haemodynamics does not predict the degree of hypertrophy regression or LVM normalisation. These results led us to reconsider the current reverse remodelling paradigm and (1) to include criteria of hypertrophy reversibility in the decision algorithm used to decide timing for the operation; and (2) to modify other prevailing factors (overweight, diabetes, etc) known to maintain LV hypertrophy.
Authors: Brenda Mendizábal; Elaine M Urbina; Richard Becker; Stephen R Daniels; Bonita E Falkner; Gilad Hamdani; Coral D Hanevold; Stephen R Hooper; Julie R Ingelfinger; Marc Lande; Lisa J Martin; Kevin Meyers; Mark Mitsnefes; Bernard Rosner; Joshua A Samuels; Joseph T Flynn Journal: Hypertension Date: 2018-09 Impact factor: 10.190
Authors: Yang Wang; Ling Li; Benjamin T Moore; Xian-Hao Peng; Xiang Fang; Joan M Lappe; Robert R Recker; Peng Xiao Journal: PLoS One Date: 2012-04-10 Impact factor: 3.240
Authors: Philipp Diehl; Alba Fricke; Laura Sander; Johannes Stamm; Nicole Bassler; Nay Htun; Mark Ziemann; Thomas Helbing; Assam El-Osta; Jeremy B M Jowett; Karlheinz Peter Journal: Cardiovasc Res Date: 2012-01-18 Impact factor: 10.787
Authors: Carmen Lopez-Sanchez; Diego Franco; Fernando Bonet; Virginio Garcia-Lopez; Amelia Aranega; Virginio Garcia-Martinez Journal: Data Brief Date: 2015-08-24