Time to solve the puzzle of heart failure with preserved ejection fraction by biomarkers, echocardiography and cardiac magnetic resonance

Abstract

\n \n \n Type of funding sources: Public grant(s) – National budget only. Main funding source(s): HEART PRESERVED\n \n \n \n HEART PRESERVED\n \n \n \n Heart failure with preserved ejection fraction (HFpEF) is now recognized as a major and growing public health problem worldwide. Clinical trials investigating different treatment strategies had disappointing results. Several biomarkers of inflammation, endothelial dysfunction, and myocardial fibrosis appeared to be promising in understanding HFpEF pathophysiology.\n Methods. We enrolled prospectively 94 patients with HFpEF in sinus rhythm \xa0(according to 2019 scoring system) (67\u2009±\u20099 yrs, 33 men). We evaluated them by 2D and speckle tracking echocardiography (STE). 80 patients had also a cardiac magnetic resonance (CMR) 1.5T evaluation. We measured LV ejection fraction (LVEF), mean E’ (E’m), E/E’ ratio, sPAP, left atrial volume indexed (LAVi), and global longitudinal stain by STE (GLS).\xa0 By CMR we evaluated LVEFcmr, LV mass, T1 mapping with mean extracellular volume (ECVm), and pre-gadolinium mean times quantification (preGDT1m) as markers of myocardial fibrosis. All patients had NTproBNP and biomarkers for systemic inflammation (IL6, cystatin C, pentraxin-3, GDF15), endothelial dysfunction: soluble E -selectin, VCAM, von Willebrand factor (vWf), and myocardial fibrosis: Galectin-3.\n Results.\xa0LVEF was 60.5\u2009±\u20096 % and LVEFcmr 61\u2009±\u20096.6%. All parameters from the scoring system were as we expected: E’m\u2009=\u20097.6\u2009±\u20091.8 cm/s, E/E’ ratio\u2009=\u200911\u2009±\u20093.4, sPAP\u2009=\u200934\u2009±\u20098 mmHg, LAVi\u2009=\u200947\u2009±\u200911 ml/m2, GLS=-18.3\u2009±\u20092.9, and NTproBNP of 282\u2009±\u2009294 pg/ml. NTproBNP significantly correlated with sPAP, LAVi, preGDT1m, ECVm, galectin-3, GDF15, and pentraxin-3 (all r\u2009>\u20090.4, p\u2009<\u20090.05). The best predictor for NTproBNP level was GDF15 (r\u2009=\u20090.4, r2\u2009=\u20090.25, p\u2009=\u20090.001).\xa0LAVi significantly corelated with E/E’ ratio, sPAP, NTproBNP, galectin-3 (r\u2009>\u20090.4, p\u2009<\u20090.05).\xa0GLS correlated with LVEFcmr, LV mass, ECVm, preGDT1m, LAVi, E/E’ ratio, NTproBNP, GDF15, vWf, Eselectin, VCAM (all r\u2009=\u20090.4, p\u2009<\u20090.05). The best predictor model for GLS was LV mass, NTproBNP, E-selectine, and vWf (r\u2009=\u20090.67, r2\u2009=\u20090.45, P\u2009<\u20090.001).\xa0sPAP was best predicted by a model composed by IL6, VCAM, LAVi (r\u2009=\u20090.5, r2\u2009=\u20090.25, p\u2009<\u20090.001).\xa0E’m significantly correlated with vWf, GHD15, VCAM, LV mass, and preGDT1 (all r\u2009>\u20090.4, p\u2009<\u20090.05), but the best predictor model included only LV mass and GDF15 (r\u2009=\u20090.57, r2\u2009=\u20090.32, P\u2009<\u20090.001).\xa0Galectin-3 significantly correlated with LAVi, preGDT1m, and NTproBNP, but the only predictor for galectin-3 level was preGDT1 (r\u2009=\u20090.4, r2\u2009=\u20090.2, p\u2009=\u20090.007).\n \n \n \n In HFpEF NTproBNP is significantly\xa0correlated\xa0with markers of inflammatory\xa0status, endothelial\xa0dysfunction, and fibrosis, but the level is mainly\xa0determined by inflammation\xa0(GDF15). Diastolic dysfunction parameters are mainly correlated with inflammatory\xa0and endothelial dysfunction biomarkers\xa0. Only LAVi was correlated with myocardial fibrosis. Sub-clinical\xa0systolic dysfunction is mainly determined by proinflamatory status and endothelial dysfunction, but not by fibrosis.\n