Dataset Information

Epigenetics in heart failure phenotypes.

ABSTRACT: Chronic heart failure (HF) is a leading clinical and public problem posing a higher risk of morbidity and mortality in different populations. HF appears to be in both phenotypic forms: HF with reduced left ventricular ejection fraction (HFrEF) and HF with preserved left ventricular ejection fraction (HFpEF). Although both HF phenotypes can be distinguished through clinical features, co-morbidity status, prediction score, and treatment, the clinical outcomes in patients with HFrEF and HFpEF are similar. In this context, investigation of various molecular and cellular mechanisms leading to the development and progression of both HF phenotypes is very important. There is emerging evidence that epigenetic regulation may have a clue in the pathogenesis of HF. This review represents current available evidence regarding the implication of epigenetic modifications in the development of different HF phenotypes and perspectives of epigenetic-based therapies of HF.

SUBMITTER: Berezin A

PROVIDER: S-EPMC4909708 | biostudies-literature | 2016 Dec

REPOSITORIES: biostudies-literature

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Epigenetics in heart failure phenotypes.

Berezin Alexander A

BBA clinical 20160530

Chronic heart failure (HF) is a leading clinical and public problem posing a higher risk of morbidity and mortality in different populations. HF appears to be in both phenotypic forms: HF with reduced left ventricular ejection fraction (HFrEF) and HF with preserved left ventricular ejection fraction (HFpEF). Although both HF phenotypes can be distinguished through clinical features, co-morbidity status, prediction score, and treatment, the clinical outcomes in patients with HFrEF and HFpEF are s ...[more]

PMID: 27335803

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Project description:BackgroundComorbidity-driven microvascular inflammation is posited as a unifying pathophysiologic mechanism for heart failure with preserved ejection fraction (HFpEF). Obesity is proinflammatory and common in HFpEF. We hypothesized that unique obesity-inflammation HFpEF phenotypes exist and are associated with differences in clinical features, fibrosis biomarkers, and functional performance.MethodsPatients (n=301) from 3 HFpEF clinical trials were studied. Unsupervised machine learning (hierarchical clustering) with obese status and 13 inflammatory biomarkers as input variables was performed. Associations of clusters with HFpEF severity and fibrosis biomarkers (PIIINP [procollagen III N-terminal peptide], CITP [C-telopeptide for type I collagen], IGFBP7 [insulin-like growth factor-binding protein-7], and GAL-3 [galectin-3]) were assessed.ResultsHierarchical clustering revealed 3 phenotypes: pan-inflammatory (n=129; 64% obese), noninflammatory (n=83; 55% obese), and obese high CRP (C-reactive protein; n=89; 98% obese). The pan-inflammatory phenotype had more comorbidities and heart failure hospitalizations; higher left atrial volume, NT-proBNP (N-terminal pro-B-type natriuretic peptide), and fibrosis biomarkers; and lower glomerular filtration rate, peak oxygen consumption, 6-minute walk distance, and active hours/day (P<0.05 for all). The noninflammatory phenotype had the most favorable values for all measures. The obese high CRP phenotype resembled the noninflammatory phenotype except for isolated elevation of CRP and lower functional performance. Hierarchical cluster assignment was independent of CRP genotype combinations that alter CRP levels and more biologically plausible than other clustering approaches. Multiple traditional analytic techniques confirmed and extended the hierarchical clustering findings.ConclusionsUnique obesity-inflammation phenotypes exist in HFpEF and are associated with differences in comorbidity burden, HFpEF severity, and fibrosis. These data support comorbidity-driven microvascular inflammation as a pathophysiologic mechanism for many but not all HFpEF patients.

Project description:AimsHeart failure (HF) with supranormal ejection fraction (HFsnEF) represents a distinct clinical entity characterized by limited treatment options and an unfavourable prognosis. Revealing its phenotypic diversity is crucial for understanding disease mechanism and optimizing patient management. We aim to identify phenotypic subgroups in HFsnEF using unsupervised clustering analysis.MethodsConsecutive hospitalized patients with a diagnosis of HF and a left ventricular ejection fraction ≥65% at baseline echocardiographic evaluations were included for analysis. We conducted unsupervised hierarchical clustering analysis on principal components (HCPC) to identify HFsnEF phenogroups using mixed data variables including demographics, HF duration, vital signs, anthropometrics, smoking/drinking status, HF aetiology, comorbid diseases, laboratory tests and echocardiographic parameters. We then employed decision tree modelling to identify parameters capable of distinguishing distinct clusters. Clinical outcomes, including all-cause death, cardiovascular (CV) death and CV readmission for different clusters, were examined.ResultsThree mutually exclusive clusters were identified from the cohort of 221 HFsnEF patients. Cluster 1 (52.5%) predominantly consisted of patients with valvular heart disease, who had larger cardiac chambers and a higher prevalence of atrial fibrillation/atrial flutter. Cluster 2 (26.2%) primarily comprised older ischaemic patients with a higher prevalence of metabolic comorbidities. Cluster 3 (21.3%) were mainly hypertrophic cardiomyopathy patients. Two clinical variables were identified that could be used to group all HFsnEF patients into one of the clusters; they were HF aetiology and comorbid diabetes. During the median follow-up of 53.4 months, 46 (20.8%) all-cause deaths occurred, among them 39 of CV causes. Seventy (31.7%) patients experienced CV readmissions. Three clusters showed distinct differences in mortality outcomes, with Cluster 1 exhibiting the highest risk of all-cause mortality [Cluster 1 vs. Cluster 2: adjusted hazard ratio (aHR) = 3.32, P = 0.022; Cluster 1 vs. Cluster 3: aHR = 3.81, P = 0.036; Cluster 2 vs. Cluster 3: aHR = 1.15, P = 0.865] and CV mortality (Cluster 1 vs. Cluster 2: aHR = 3.73, P = 0.022; Cluster 1 vs. Cluster 3: aHR = 4.27, P = 0.020; Cluster 2 vs. Cluster 3: aHR = 1.15, P = 0.870). CV readmission risk was comparable among the three clusters (Cluster 1 vs. Cluster 2: aHR = 0.82, P = 0.590; Cluster 1 vs. Cluster 3: aHR = 1.04, P = 0.900; Cluster 2 vs. Cluster 3: aHR = 1.28, P = 0.580).ConclusionsIn a heterogeneous HFsnEF cohort, three clusters were identified by unsupervised HCPC with distinct clinical characteristics and outcomes.

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Epigenetics in heart failure phenotypes.

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Epigenetics in heart failure phenotypes.

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