The future of heart failure with preserved ejection fraction

Abstract

Heart failure (HF) with preserved ejection fraction (HFpEF) is a multi-organ, systemic syndrome that involves multiple cardiac and extracardiac pathophysiologic abnormalities. Because HFpEF is a heterogeneous syndrome and resistant to a “one-size-fits-all” approach it has proven to be very difficult to treat. For this reason, several research groups have been working on methods for classifying HFpEF and testing targeted therapeutics for the HFpEF subtypes identified. Apart from conventional classification strategies based on comorbidity, etiology, left ventricular remodeling, and hemodynamic subtypes, researchers have been combining deep phenotyping with innovative analytical strategies (e.g., machine learning) to classify HFpEF into therapeutically homogeneous subtypes over the past few years. Despite the growing excitement for such approaches, there are several potential pitfalls to their use, and there is a pressing need to follow up on data-driven HFpEF subtypes in order to determine their underlying mechanisms and molecular basis. Here we provide a framework for understanding the phenotype-based approach to HFpEF by reviewing (1) the historical context of HFpEF; (2) the current HFpEF paradigm of comorbidity-induced inflammation and endothelial dysfunction; (3) various methods of sub-phenotyping HFpEF; (4) comorbidity-based classification and treatment of HFpEF; (5) machine learning approaches to classifying HFpEF; (6) examples from HFpEF clinical trials; and (7) the future of phenomapping (machine learning and other advanced analytics) for the classification of HFpEF.

Increased endothelium activation and leakage do not promote diastolic dysfunction in mice fed with a high fat diet and treated with L-NAME

“…These results together with the negative results of clinical trials targeting the NO/cGMP pathway weakens Paulus et al paradigm 5 and suggest that neither endothelial dysfunction nor cardiac inflammation is causing HFpEF . However, considering HFpEF is an heterogeneous disease with multiple pheno-groups 28 , these results needs to be verified in other animal models of HFpEF. Indeed, even though endothelial dysfunction does not seem to be necessary for diastolic dysfunction to develop upon a HFD + L-NAME regimen, EC protection was previously shown to prevent the occurrence of both diastolic and systolic dysfunction in other setting including pressure overload and hypertension 11 .…”

Traditional Chinese medicine for heart failure with preserved ejection fraction: clinical evidence and potential mechanisms

“…Modern research shows that the transition from HFpEF to HFrEF is rare. Compared with HFrEF, HFpEF has higher comorbidity and noncardiovascular mortality ( Heinzel and Shah, 2022 ). Therefore, the guide proposes different management models for HFpEF and HFrEF.…”

“…Recent studies have shown that myocardial injury in HFpEF is not primary but rather systemic inflammation caused by arterial hypertension or metabolic diseases such as diabetes and obesity. It eventually leads to coronary artery microvascular inflammation, dysfunction, subendocardial ischaemia, myocardial cellular mechanisms and metabolic changes ( Heinzel and Shah, 2022 ).…”

“…Given the high mortality and rehospitalization rate of HFpEF ( Schrutka et al, 2022 ), how to use drugs and improve the prognosis of patients has also become the focus of our attention. The current treatments in Western medicine include SGLT-2 inhibitors, which can regulate diabetic comorbid disease and improve the quality of life of patients ( Heinzel and Shah, 2022 ); sacubatril valsartan, which can prevent the deterioration of renal function and reduce the risk of cardiovascular death ( Cui et al, 2022 ); corresponding anti-inflammatory strategies, which can improve systemic microvascular inflammation ( Omote et al, 2022 ); pirfenidone, which can reduce TGF-β and improve myocardial fibrosis ( Lewis et al, 2021 ); and spironolactone, which can regulate cardiac function ( Beldhuis et al, 2021 ). Although its treatment takes into account the comorbidities and prognosis of HFpEF, it also has some limitations.…”

Predicting Physiological Response in Heart Failure Management: A Graph Representation Learning Approach using Electronic Health Records

“…The Lab tests DBP, SBP and SVR are considered as risk factors imperative in HF prognosis 33 and are routinely monitored as part of the EHR, so the good results are not surprising. The HF cohort predominantly consists of older patients (e.g., 74 was the most prevalent age in our ACEI cohort), who are also more likely to have multiple comorbidities 34 , such as hypertension, diabetes mellitus, atrial fibrillation, and hyperlipidemia, which further contribute to the heterogeneity of HF 35 . So, using the Comorbidity information is beneficial as indicated by its satisfactory performance and identifying the combinations of the comorbidities corresponding to the different phenogroups as a next step could lead to targeted HF treatment.…”