Chronic HF remains a deadly clinical syndrome associated with considerable loss of quality life and high socioeconomic burden. Dyspnea on exertion and exercise intolerance are the leading symptoms but a myriad of clinical manifestations from the early stages of the HF syndrome (e.g., fatigue) to late-stage HF (e.g., loss of appetite, cachexia) can occur. Having emerged as a common and growing type of HF, HFpEF (defined most recently by international consensus as HF with EF ≥ 50% [22]) currently accounts for 50% of all HF and is projected to grow in proportion of HF over the next few decades. Patients with HFpEF generally do not respond to strategies known to improve the prognosis in patients with HF and reduced EF (HFrEF). The reasons proposed for this include a variety of different underlying pathomechanisms (that may not be very responsive to neurohormonal antagonists, in particular), a much greater heterogeneity in therapeutic response, and a higher prevalence of non-cardiac comorbidities compared with HFrEF. It is now clear that HFpEF is not an early form of HFrEF [23]; systematic longitudinal studies have shown that the transition from HFpEF to HFrEF is rare [24]. The lack of prognostic benefit in HFpEF from current HFrEF strategies suggests novel disease mechanisms but also questions the classic concept of myocardial injury as the main driver of disease progression in HFpEF.

In the classic mechanistic concept of HFpEF, the heart was the primary source of the syndrome, with left ventricular (LV) hypertrophy (LVH) and diastolic dysfunction as the main drivers, triggered by systemic hypertension, with contributions from risk factors such as coronary artery disease (CAD). This mechanistic concept arose from early studies in the 1970s of diastolic dysfunction in the cardiac catheterization laboratory, and later by various case series showing impaired filling of the LV in patients with HF and a normal EF [25]. The field was heavily influenced by hypertrophic cardiomyopathy (HCM); indeed, HFpEF was viewed as a forme fruste of HCM in that it was similar pathophysiologically but did not meet criteria for HCM because there was no family history, no genetic abnormalities, and a cause of LVH (hypertension) was present in the majority of patients. The lack of dedicated HFpEF programs also likely impaired this stage of HFpEF understanding because clinicians were not seeing the full extent of HFpEF in the community; rather, it was often patients with severe HFpEF or specific cardiomyopathies (e.g., cardiac amyloidosis, HCM) that were presenting to HF clinics with “diastolic HF” [26]. Furthermore, over the past 50 years, we have witnessed an epidemiological transition from uncontrolled hypertension and smoking (major risk factors for LVH) and high rates of CAD to a better control of these risk factors and an explosion of morbid obesity, diabetes, atrial fibrillation, coupled with an aging population. Whether HFpEF itself has changed due to these epidemiological transitions, or whether it was always the case, it is now clear that HFpEF is not simply cardiac-centric but instead a systemic syndrome, with multiple involved organs; not only the heart but the lungs, liver, adipose tissue, skeletal muscles, and kidneys are all variably involved in individual patients with HFpEF [27].

A primary cardiac insult is the trigger for HFrEF (e.g., myocardial infarction, myocarditis, genetic abnormality, chemotherapy). In HFpEF, however, cardiomyocyte cell death and severe loss of contractility at rest are not commonly observed. Instead, cardiac function in HFpEF is characterized by impaired cardiac filling and altered diastolic properties (stiffness) of the LV, which result in increased LV filling pressure, congestion, and dyspnea on exertion. The underlying pathomechanisms are still not completely resolved, but a leading theory is that myocardial injury in HFpEF is not primary but rather secondary to comorbidity-induced stress on the endothelium [28].

According to a leading conceptual framework of HFpEF, largely based on preclinical disease models, systemic inflammation, triggered by arterial hypertension and metabolic disease states such as diabetes and obesity, leads to coronary microvascular inflammation and dysfunction, subendocardial ischemia, and altered cardiomyocyte mechanics and metabolism (as reviewed in [29]). Thus, HFpEF is considered a result of a multisystem disorder and is strongly associated with advanced age, which may reflect cumulative effects of an increasing number and duration of systemic comorbidities.

The multitude of comorbidities that lead to HFpEF, coupled with the multi-organ, systemic nature of the HFpEF syndrome, contribute to its heterogeneity. Patients with HFpEF share signs and symptoms of HF and echocardiographic features or biomarker evidence of elevated left atrial pressure with preserved LVEF as the main criteria within the current HF classification [30]. However, clinical evidence from prospective trials and observational studies suggests heterogeneity in pathophysiological triggers and clinical presentation with relevance for further diagnostics and therapy. For several decades HCM has been a model for diastolic dysfunction related to structural heart disease, but HCM is found in only a minority (~5%) of HFpEF patients. Based on findings in HCM studies, LVH was considered to be a major contributor to HFpEF because hypertension is a very common comorbidity and has been considered a trigger for HFpEF. Yet, the association between the degree of LVH and diastolic dysfunction in clinical studies is weak, and LVH is only found in about half of the patients with HFpEF [31], which suggests that even myocardial remodeling is heterogeneous in HFpEF.

Recently, HFpEF related to cardiac amyloidosis with accumulation of misfolded proteins in the extracellular matrix has moved into focus, and even though cardiac amyloidosis is likely to be present in a small but relevant fraction of HFpEF patients (~5%, [32]), it is an example of how there may be subtypes of HFpEF that have unique features on cardiac imaging, a specific confirmatory diagnostic approach, and targeted therapy. The common form of HFpEF has been associated with metabolic disease such as obesity or diabetes. Indeed, seminal studies in human myocardium from HFpEF patients have suggested that diabetes mellitus, which is present in almost half of HFpEF patients (45%, [33]), may induce specific alterations in myocardial passive stiffness not observed in non-diabetic HFpEF tissue [34, 35]. In this context, HFpEF is now considered one manifestation of diabetic cardiomyopathy [36] with a higher prevalence of myocardial hypertrophy and fibrosis and a worse prognosis. Chronic kidney disease (CKD; defined as estimated glomerular filtration rate < 60 ml/min/1.73 m²), present in approximately 50% of HFpEF patients [37], may be another discriminator. For instance, in a recent Japanese study, patients with moderate CKD but not patients with manifest LVH profited from renin–angiotensin–aldosterone system inhibitors [38]. Varying definitions for the cut-off for a preserved EF in randomized trials (between 40 and 50%) have added to the perceived variability also between HFpEF cohorts. For instance, the fraction of patients with ischemic heart disease was higher in studies with a lower EF cut-off (≥ 40%) as in the CHARM-Preserved (65% of patients) and EMPEROR-Preserved (36% of patients) trials compared with studies with a higher EF cut-off (≥ 45%) as in the PARAGON (23% of patients with ischemic heart disease) and PARAMOUNT (21% of patients) trials [39‐42].

A variety of methods for classification of HFpEF have been proposed, and each may be useful clinically for selecting appropriate therapies and designing targeted clinical trials. These include clinical (etiological, or primary comorbidity driving HFpEF in a particular patient), pathophysiological (primary pathophysiology driving HFpEF in a particular patient), myocardial, type of clinical presentation, and data-driven approaches to the classification of HFpEF (Table 1). Examples of methods for identification of specific HFpEF subtypes, and how these strategies can lead to novel therapies, are discussed next.

The different clinical manifestations of HFpEF in clinical trials and registries, as well as the failure of generalized medical approaches such as renin–angiotensin–receptor signaling cascade blockers to improve the prognosis in HFpEF cohorts, have emphasized the need for a more differentiated therapeutic strategy. A proposed treatment strategy suggests the identification of leading comorbidities based on the clinical phenotype, which would then guide therapeutic interventions [66]. This approach is in line with current European Society of Cardiology (ESC) guideline recommendations to treat etiologies and cardiovascular and non-cardiovascular comorbidities in HFpEF [67]. However, whether specific treatment of such selected subgroups improves the prognosis in HFpEF remains to be determined. For instance, treating arterial hypertension can reduce the risk for HF, including HFpEF [68]. However, while lowering elevated systolic blood pressure in patients with manifest HFpEF was associated with reduced hospitalization rates it did not lower all-cause or cardiovascular mortality [69].

The majority of HFpEF patients have increased pulmonary artery pressure (pulmonary hypertension [PH]), although in most this is typically pulmonary venous hypertension; however, there are some HFpEF patients who have combined post- and pre-capillary PH. Treatment using specific drugs approved for pulmonary artery hypertension in unselected patients with HFpEF has yielded mixed results and is currently not recommended [70]. Stimulators of the soluble guanyl cyclase (enhancing cGMP-signaling) have been effective in patients with primary pulmonary artery hypertension (riociguat [71]) and also in HFrEF (vericiguat [72]). Yet, vericiguat demonstrated neutral results in HFpEF patients in the VITALITY-HFpEF [73] and SOCRATES-Preserved trials [74]; however, both trials did not differentiate patients without versus with PH.

Dietary restriction in obese patients improves diastolic function [75], and a reduction in calorie intake by ~400 kcal/day increases exercise capacity and quality of life in elderly obese HFpEF patients [76]. Again, the impact on prognosis has yet to be established. Ongoing and future trials of drugs that result in significant weight loss (e.g., GLP‑1 receptor agonists) will hopefully answer the question of whether weight loss improves outcomes in HFpEF. In summary, in patients with HFpEF, treatment of contributing risk factors may or may not be sufficient to treat the cardiac and systemic alterations that they have accumulated from these risk factors.

As mentioned earlier, multiple studies have now applied data-driven approaches (“phenomapping”) to HFpEF classification. These studies have used machine learning analytic techniques similar to our original phenomapping study and have shown that the HFpEF subtypes identified have different clinical features and outcomes (Fig. 3; [7]). Nonetheless, the study by Shah and colleagues was only the initial “proof of concept” that HFpEF is a truly heterogeneous syndrome (as shown in the heatmap in Fig. 3, left panel), warranting larger-scale, multicenter investigations to advance the science of identifying novel HFpEF subtypes and treatment targets using next-generation phenomics. Importantly, machine learning analysis of deeply phenotyped HFpEF cohorts is only the starting point for further investigation into HFpEF subtypes. While several HFpEF phenomapping studies have been published and have agreed on up to five common HFpEF sub-phenotypes (Fig. 4; [77]), additional research is required to determine the pathophysiological and molecular mechanisms underlying each HFpEF subtype.

As an example, since the initial HFpEF phenomapping study [7], Shah et al. have probed further into the three identified HFpEF subtypes by investigating underlying disease mechanisms and designing and conducting targeted clinical trials in these subtypes, as shown in Table 2. In addition, once HFpEF subtypes have been identified, analytical approaches such as natural language processing and supervised machine learning can be used to assist with automated identification of patients within specific HFpEF subtypes, which will assist with enrollment in future targeted HFpEF clinical trials and targeting of specific therapies to HFpEF subtypes. For example, natural language processing of unstructured electronic health record data has been used to automate the identification of eligible patients for an HFpEF clinical trial (PARAGON; [78]). In addition, large-scale machine learning analyses of national electronic health record claims data have been used to develop a prediction model for amyloidogenic transthyretin cardiomyopathy to improve/automate clinical recognition of this treatable HFpEF subtype [79]. Deep learning machine learning models have also been used for the automated identification of amyloidogenic transthyretin cardiomyopathy using electrocardiography and echocardiography [80].

Various types of -omics data can be analyzed with data reduction techniques with subsequent identification of overrepresented biological pathways, which can provide insight into underlying biological mechanisms of HFpEF and its subtypes. This has been done using principal components analysis and weighted coexpression network analyses ([9]; Fig. 5). These techniques reduce high-dimensional data into a low-dimensional data space, summarized by eigenvalues. Eigenvalues can then be used in conventional multivariable regression and mediation analyses to determine associations and perform causal inference with particular HFpEF subtypes.

Despite the rapid rise of machine learning and -omics studies in HF, including HFpEF, there are several challenges to these types of studies that must be considered when evaluating their clinical utility in the classification (sub-phenotyping) of HFpEF and in understanding underlying molecular mechanisms. Table 3 lists key steps in performing machine learning analyses relevant to classification of HFpEF. Table 4 lists various potential problems with these types of studies (e.g., lack of external validation, single timepoint assessments, disease progression bias, cohort bias, feature bias, and publication bias) and offers potential solutions for overcoming these limitations.

Several ongoing and recently completed HFpEF clinical trials have taken advantage of sub-phenotyping of HFpEF in order to target therapeutics to particular HFpEF subtypes in an attempt to improve chances for HFpEF clinical trial success. For example, in the EMBARK-HFpEF trial (open-label proof-of-concept study of mavacamten [myosin inhibitor] in HFpEF [NCT04766892]), only patients with LVEF > 60%, LV hypertrophy, and elevated biomarkers (elevated natriuretic peptides or troponin) are allowed entry into the trial. These HFpEF patients are most similar to patients with genetic/familiar forms of HCM and therefore are hypothesized to be the patients most likely to benefit (and least likely to have adverse effects) from mavacamten therapy.

In the SERENADE trial (NCT03153111), which is a randomized controlled trial of macitentan (a dual endothelin receptor A and B antagonist) in patients with HFpEF, elevated natriuretic peptides, cardiac remodeling (LVH or LA enlargement), and evidence of pulmonary vascular disease (invasive defined elevation in pulmonary vascular resistance or diastolic pulmonary gradient; or elevated pulmonary artery pressure with evidence of right ventricular dysfunction) are allowed entry into the trial. These patients are hypothesized to be those who will benefit most from macitentan. To further select patients who may benefit, SERENADE included a 4-week placebo run-in phase (to ensure clinical stability) and a 5-week macitentan run-in phase (to exclude patients who develop early fluid retention in response to macitentan). Only patients who qualify for the trial and make it through these two run-in phases were randomized in the trial.

In the recently completed REDUCE LAP-HF II trial ([81]; a phase 3, pivotal, multicenter trial of 626 HFpEF patients randomized 1:1 to an atrial shunt device vs. sham control), all patients were required to have documented evidence of elevated pulmonary capillary wedge pressure (> 25 mm Hg during exercise) on their screening right heart catheterization study. Furthermore, patients with evidence of greater than mild right ventricular dysfunction, greater than mild tricuspid regurgitation, elevated pulmonary vascular resistance (PVR > 3.5 WU), or other reasons to suspect poor outcomes with the device were excluded. Thus, REDUCE LAP-HF II was an enrichment trial (a type of precision medicine trial; [82]). Nevertheless, despite the careful patient selection process, the overall trial results were neutral. However, further post hoc analyses demonstrated that patients with peak exercise pulmonary vascular resistance < 1.74 WU appeared to benefit from the device (Fig. 6; [83]). Thus, future precision medicine trials of atrial shunt devices and procedures in HFpEF may benefit from only including those patients with the ability to vasodilate the pulmonary vasculature during exertion.

Current phenomapping approaches for HFpEF are largely based on clinical data available from demographics and routine clinical parameters such as medical history, medication history, routine blood tests, as well as electrocardiographic and echocardiographic read-outs [7, 15]. Future HFpEF phenomapping studies ideally will be planned prospectively and have already started at various centers across the world (e.g., the US National Institutes of Health HeartShare Study [www.​HeartShareStudy.​org]). In addition, advanced imaging, e.g., machine learning-based (magnetic resonance) image analysis and deep-learning algorithms for echocardiographic data may provide incremental prognostic value in HFpEF [84, 85]. Metabolic profiling quantifying blood serum metabolites by liquid chromatography–tandem mass spectrometry and proton-nuclear magnetic resonance spectroscopy and evaluation of their response to therapy (e.g., exercise training) has also been used to characterize HFpEF patients [86, 87]. An interesting approach is also to combine medical history and multi-omics analyses (e.g., serum metabolome and gut microbiome in cardiometabolic disease patients) to compute the response to drugs in patient subgroups [88].