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Clin Res Cardiol ; 2020 Apr 06.
Artigo em Inglês | MEDLINE | ID: mdl-32253507


BACKGROUND: Plasma volume (PV) estimated from Duarte's formula (based on hemoglobin/hematocrit) has been associated with poor prognosis in patients with heart failure (HF). There are, however, limited data regarding the association of estimated PV status (ePVS) derived from hemoglobin/hematocrit with clinical profiles and study outcomes in patients with HF and preserved ejection fraction (HFpEF). METHODS AND RESULTS: Patients from North and South America enrolled in the Treatment of Preserved Cardiac Function Heart Failure with an Aldosterone Antagonist trial (TOPCAT) with available hemoglobin/hematocrit data were studied. The association between ePVS (Duarte formula and Hakim formula) and the composite of cardiovascular mortality, HF hospitalization, or aborted cardiac arrest was assessed. Among 1747 patients (age 71.6 years; males 50.1%), mean ePVS derived from Duarte formula was 4.9 ± 1.0 mL/g. Higher Duarte-derived ePVS was associated with prior HF admission, diabetes, more severe congestion, poor renal function, higher natriuretic peptide level, and E/e'. After adjustment for potential covariates including natriuretic peptide, higher Duarte-derived ePVS was associated with an increased rate of the primary outcome [highest vs. lowest ePVS quartile: adjusted-HR (95%CI) = 1.79 (1.28-2.50), p < 0.001]. Duarte-derived ePVS improved prognostic performance on top of clinical and routine variables (including natriuretic peptides) (NRI = 11, p < 0.001), whereas Hakim-derived ePVS did not (p = 0.59). The prognostic value of Duarte-derived ePVS was not modified by renal function (P interaction > 0.10 for all outcomes). CONCLUSION: ePVS from Duarte's formula was associated with congestion status and improved risk stratification regardless of renal function. Our findings suggest that Duarte-derived ePVS is a useful congestion variable in patients with HFpEF.

ESC Heart Fail ; 2020 Mar 13.
Artigo em Inglês | MEDLINE | ID: mdl-32167681


AIMS: Activation of the renin-angiotensin-aldosterone system plays an important role in the pathophysiology of heart failure (HF) and has been associated with poor prognosis. There are limited data on the associations of renin and aldosterone levels with clinical profiles, treatment response, and study outcomes in patients with HF. METHODS AND RESULTS: We analysed 2,039 patients with available baseline renin and aldosterone levels in BIOSTAT-CHF (a systems BIOlogy study to Tailored Treatment in Chronic Heart Failure). The primary outcome was the composite of all-cause mortality or HF hospitalization. We also investigated changes in renin and aldosterone levels after administration of mineralocorticoid receptor antagonists (MRAs) in a subset of the EPHESUS trial and in an acute HF cohort (PORTO). In BIOSTAT-CHF study, median renin and aldosterone levels were 85.3 (percentile25-75 = 28-247) µIU/mL and 9.4 (percentile25-75 = 4.4-19.8) ng/dL, respectively. Prior HF admission, lower blood pressure, sodium, poorer renal function, and MRA treatment were associated with higher renin and aldosterone. Higher renin was associated with an increased rate of the primary outcome [highest vs. lowest renin tertile: adjusted-HR (95% CI) = 1.47 (1.16-1.86), P = 0.002], whereas higher aldosterone was not [highest vs. lowest aldosterone tertile: adjusted-HR (95% CI) = 1.16 (0.93-1.44), P = 0.19]. Renin and/or aldosterone did not improve the BIOSTAT-CHF prognostic models. The rise in aldosterone with the use of MRAs was observed in EPHESUS and PORTO studies. CONCLUSIONS: Circulating levels of renin and aldosterone were associated with both the disease severity and use of MRAs. By reflecting both the disease and its treatments, the prognostic discrimination of these biomarkers was poor. Our data suggest that the "point" measurement of renin and aldosterone in HF is of limited clinical utility.

Biomarkers ; 25(2): 201-211, 2020 Mar.
Artigo em Inglês | MEDLINE | ID: mdl-32063068


Background: Heart failure with preserved ejection fraction (HFpEF) is a heterogeneous syndrome for which clear evidence of effective therapies is lacking. Understanding which factors determine this heterogeneity may be helped by better phenotyping. An unsupervised statistical approach applied to a large set of biomarkers may identify distinct HFpEF phenotypes.Methods: Relevant proteomic biomarkers were analyzed in 392 HFpEF patients included in Metabolic Road to Diastolic HF (MEDIA-DHF). We performed an unsupervised cluster analysis to define distinct phenotypes. Cluster characteristics were explored with logistic regression. The association between clusters and 1-year cardiovascular (CV) death and/or CV hospitalization was studied using Cox regression.Results: Based on 415 biomarkers, we identified 2 distinct clusters. Clinical variables associated with cluster 2 were diabetes, impaired renal function, loop diuretics and/or betablockers. In addition, 17 biomarkers were higher expressed in cluster 2 vs. 1. Patients in cluster 2 vs. those in 1 experienced higher rates of CV death/CV hospitalization (adj. HR 1.93, 95% CI 1.12-3.32, p = 0.017). Complex-network analyses linked these biomarkers to immune system activation, signal transduction cascades, cell interactions and metabolism.Conclusion: Unsupervised machine-learning algorithms applied to a wide range of biomarkers identified 2 HFpEF clusters with different CV phenotypes and outcomes. The identified pathways may provide a basis for future research.Clinical significanceMore insight is obtained in the mechanisms related to poor outcome in HFpEF patients since it was demonstrated that biomarkers associated with the high-risk cluster were related to the immune system, signal transduction cascades, cell interactions and metabolismBiomarkers (and pathways) identified in this study may help select high-risk HFpEF patients which could be helpful for the inclusion/exclusion of patients in future trials.Our findings may be the basis of investigating therapies specifically targeting these pathways and the potential use of corresponding markers potentially identifying patients with distinct mechanistic bioprofiles most likely to respond to the selected mechanistically targeted therapies.