Risk Stratification Towards Precision Medicine in Heart Failure - Current Progress and Future Perspectives

Epigenetic regulation of heart failure

PURPOSE OF REVIEW

The studies on chromatin-modifying enzymes and how they respond to different stimuli within the cell have revolutionized our understanding of epigenetics. In this review, we provide an overview of the recent studies on epigenetic mechanisms implicated in heart failure.

RECENT FINDINGS

We focus on the major mechanisms and the conceptual advances in epigenetics as evidenced by studies in humans and mouse models of heart failure. The significance of epigenetic modifications and the enzymes that catalyze them is also discussed. New findings from the studies of histone lysine demethylases demonstrate their significance in regulating fetal gene expression, as well as their aberrant expression in adult hearts during HF. Similarly, the relevance of histone deacetylases inhibition in heart failure and the role of HDAC6 in cardio-protection are discussed. Finally, the role of LMNA (lamin A/C), a nuclear membrane protein that interacts with chromatin to form hundreds of large chromatin domains known as lamin-associated domains (LADs), and 3D genome structure in epigenetic regulation of gene expression and heart failure is discussed.

SUMMARY

Epigenetic modifications provide a mechanism for responding to stress and environmental variation, enabling reactions to both external and internal stimuli, and their dysregulation can be pathological as in heart failure. To gain a thorough understanding of the pathological mechanisms and to aid in the development of targeted treatments for heart failure, future research on studying the combined effects of numerous epigenetic changes and the structure of chromatin is warranted.

Associations of long-term mortality with serum uric acid at admission in acute decompensated heart failure with different phenotypes

BACKGROUND AND AIMS

It remains unclear whether the long-term prognostic value of serum uric acid (SUA) at admission differs in acute decompensated heart failure (HF) patients across the spectrum of left ventricular ejection fraction (EF).

METHODS AND RESULTS

In 2375 patients (38.9% women; mean age, 68.8 years), we assessed the risk of long-term (>1 year) all-cause mortality associated with per 1-SD increase in SUA at admission, using multivariable Cox regression in HF with preserved (HFpEF), mildly reduced (HFmrEF) and reduced (HFrEF) EF. During a median follow-up of 4.1 years, the long-term mortality rate was 39.9%. In all patients, the multivariable-adjusted hazard ratio (HR) expressing the risk of long-term mortality associated with SUA was 1.18 (95% CI, 1.11-1.26; P < 0.001). Compared with the low tertile of the SUA distribution, the sex- and age-adjusted cumulative incidence of long-term mortality was higher in the top tertile. In patients with HFpEF and HFrEF, SUA predicted the risk of long-term mortality with HRs amounting to 1.12 (95% CI, 1.02-1.21; P = 0.012) and 1.28 (95% CI, 1.12-1.47; P < 0.001), respectively. However, there were no associations between the risk of mortality and SUA in HFmrEF. Furthermore, age, sex, NYHA class, and the prevalence of coronary heart disease interacted significantly with SUA for predicting long-term mortality.

CONCLUSION

Higher levels of SUA at admission were associated with higher risk of long-term mortality in patients with different HF subtypes. The risk conferred by SUA was age and sex dependent. Our observations highlight that measuring SUA at admission may help to improve risk stratification.

Exploring and Identifying Prognostic Phenotypes of Patients with Heart Failure Guided by Explainable Machine Learning

Identifying patient prognostic phenotypes facilitates precision medicine. This study aimed to explore phenotypes of patients with heart failure (HF) corresponding to prognostic condition (risk of mortality) and identify the phenotype of new patients by machine learning (ML). A unsupervised ML was applied to explore phenotypes of patients in a derivation dataset (n = 562) based on their medical records. Thereafter, supervised ML models were trained on the derivation dataset to classify these identified phenotypes. Then, the trained classifiers were further validated on an independent validation dataset (n = 168). Finally, Shapley additive explanations were used to interpret decision making of phenotype classification. Three patient phenotypes corresponding to stratified mortality risk (high, low, and intermediate) were identified. Kaplan−Meier survival curves among the three phenotypes had significant difference (pairwise comparison p < 0.05). Hazard ratio of all-cause mortality between patients in phenotype 1 (n = 91; high risk) and phenotype 3 (n = 329; intermediate risk) was 2.08 (95%CI 1.29−3.37, p = 0.003), and 0.26 (95%CI 0.11−0.61, p = 0.002) between phenotype 2 (n = 142; low risk) and phenotype 3. For phenotypes classification by random forest, AUCs of phenotypes 1, 2, and 3 were 0.736 ± 0.038, 0.815 ± 0.035, and 0.721 ± 0.03, respectively, slightly better than the decision tree. Then, the classifier effectively identified the phenotypes for new patients in the validation dataset with significant difference on survival curves and hazard ratios. Finally, age and creatinine clearance rate were identified as the top two most important predictors. ML could effectively identify patient prognostic phenotypes, facilitating reasonable management and treatment considering prognostic condition.

Left Atrial Myopathy in Heart Failure With Preserved Ejection Fraction

The left atrium (LA) plays an important role in facilitating left ventricular (LV) filling by acting as a reservoir, passive conduit, and active booster pump, as well as a regulator of blood volume through A-type natriuretic peptide secretion in response to stimulation by mechanical stretch of the cavity. LA myopathy has emerged as one of the most important non-LV contributors to disease progression in heart failure with preserved ejection fraction (HFpEF). LA dysfunction is common in HFpEF and is associated with more severe pulmonary vascular disease and right ventricular dysfunction, and increases the risk of incident atrial fibrillation or atrial functional mitral regurgitation, leading to limitations in cardiac output reserve and reduced exercise capacity. LA deformation assessed by 2-dimensional speckle-tracking echocardiography is useful for estimating abnormal hemodynamics or exercise capacity, discriminating HFpEF from non-cardiac dyspnea and is an independent predictor of adverse outcome in HFpEF. Thus, interventions directly targeting LA myopathy may improve outcomes in HFpEF with LA myopathy. This review provides information regarding the physiology of the LA in patients with HFpEF and discusses the importance of evaluation of LA function, management issues, and future directions through ongoing trials of medical interventions.