Towards precision medicine in heart failure

Abstract

The number of therapies for heart failure (HF) with reduced ejection fraction has nearly doubled in the past decade. In addition, new therapies for HF caused by hypertrophic and infiltrative disease are emerging rapidly. Indeed, we are on the verge of a new era in HF in which insights into the biology of myocardial disease can be matched to an understanding of the genetic predisposition in an individual patient to inform precision approaches to therapy. In this Review, we summarize the biology of HF, emphasizing the causal relationships between genetic contributors and traditional structure-based remodelling outcomes, and highlight the mechanisms of action of traditional and novel therapeutics. We discuss the latest advances in our understanding of both the Mendelian genetics of cardiomyopathy and the complex genetics of the clinical syndrome presenting as HF. In the phenotypic domain, we discuss applications of machine learning for the subcategorization of HF in ways that might inform rational prescribing of medications. We aim to bridge the gap between the biology of the failing heart, its diverse clinical presentations and the range of medications that we can now use to treat it. We present a roadmap for the future of precision medicine in HF.

Fig. 1 |. Mechanisms and physiology of the failing heart.

The initiation of heart failure occurs at the level of the myocardium and fundamentally at the level of the cardiomyocyte, secondary to complex and/or Mendelian disease. a | Pathology within the myocardium can include reactive or replacement myocardial interstitial fibrosis, microvascular rarefaction characterized by a decrease in microvascular density, and cardiomyocyte hypertrophy and apoptosis. b | At the level of the cardiomyocyte, important mechanisms include decreased excitation–contraction coupling with impaired Ca²⁺ handling, changes in energetics with decreased fatty acid β-oxidation and increased glycolysis, increased oxidative stress and damaging X-reactive oxygen species (X-ROS) signalling, and impaired contractility secondary to pathogenic variants in genes encoding sarcomeric proteins. c | These pathogenic changes lead to structural remodelling of the heart, including dilatation or hypertrophy of the left ventricle, causing a dilated cardiomyopathy (characterized by reduced contractility of the myocardium) or a restrictive cardiomyopathy (with increased filling pressures and decreased stroke volume), respectively. These pathogenic changes lead to further maladaptive systemic responses and disease progression. LV, left ventricular; RYR2, ryanodine receptor 2; SERCA2, sarcoplasmic–endoplasmic reticulum Ca²⁺ ATPase 2; SR, sarcoplasmic reticulum; TCA, tricarboxylic acid.

Fig. 2 |. Heart failure therapeutics and their mechanisms of effect.

a | In heart failure with reduced ejection fraction, therapies with proven benefit have largely focused on preventing the maladaptive systemic response that arises as a secondary process to the primary injury at the level of the cardiomyocyte and myocardium. With increased circulating levels of adrenaline, noradrenaline, angiotensin II, renin and aldosterone in heart failure, therapies such as a β-blocker, angiotensin-converting enzyme inhibitor (ACEi), angiotensin-receptor blocker (ARB), angiotensin-receptor blocker–neprilysin inhibitor (ARNI) or mineralocorticoid-receptor antagonist (MRA) work to inhibit the maladaptive effect. Maladaptive systemic responses lead to sinus tachycardia, which further increases the myocardial oxygen demand and impairs myocardial perfusion. β-Blocker or ivabradine therapy decreases myocardial oxygen demand and improves diastolic filling by reducing heart rate. Agents such as hydralazine–nitrates, ACEi, ARB and ARNI also work to promote systemic vasodilatation, which decreases cardiac afterload, thereby augmenting cardiac output and peripheral perfusion. b | Agents acting at the level of the myocardium can improve adverse cellular signalling through inhibition of the downstream effects of angiotensin (AT) receptor, β₁-adrenergic receptor (β₁-AR) and mineralocorticoid receptor (MR) agonism. Fewer therapies act directly to improve myocardial contractile function of the sarcomere, but omecamtiv mecarbil works as a myosin activator. Sodium–glucose cotransporter 2 inhibitor (SGLT2i) therapies have a direct effect on renal glucose excretion, but discoveries have highlighted additional mechanisms in mediating sodium–hydrogen exchanger 1 (NHE1) and sarcomere contractility as well as facilitating a more favourable energetic metabolism. Therapies that act on nitric oxide (NO) and cGMP signalling include vericiguat, which augments cGMP signalling, thereby enhancing the downstream cardioprotective effects of NO. Hydralazine–nitrates act by augmenting NO signalling via systemic effects on cardiac afterload as well as acting directly at the level of the myocardium. c | ARNI therapies work to increase the protective responses to heart failure via neprilysin inhibition, thereby augmenting natriuretic peptide and other kidney-derived peptide signalling. d | Targeted therapies for specific causes of restrictive cardiomyopathy include mavacamten for hypertrophic cardiomyopathy, tafamidis and patisiran for transthyretin-associated cardiac amyloidosis and agalsidase beta for Fabry disease. RYR2, ryanodine receptor 2; SERCA2, sarcoplasmic–endoplasmic reticulum Ca²⁺ ATPase 2; SR, sarcoplasmic reticulum; TCA, tricarboxylic acid.

Fig. 3 |. From Mendelian to complex disease in heart failure.

Monogenic causes of cardiomyopathy in a Mendelian inheritance are caused by a pathogenic genetic variant that can result in various forms of cardiomyopathy, such as hypertrophic cardiomyopathy (HCM), dilated cardiomyopathy (DCM), arrhythmogenic cardiomyopathy (ACM) or left ventricular non-compaction cardiomyopathy (LVNC). Complex disease patterns of inheritance are influenced by millions of genetic variants, each with a small effect, in addition to environmental causes of disease. For example, the polygenic contribution to heart failure has been described by Shah and colleagues, who conducted a genome-wide association study using data from 47,309 patients with heart failure and 930,014 control individuals gathered from 26 studies and identified 11 genomic loci associated with heart failure (displayed on the Manhattan plot). This polygenic influence of disease modifies monogenic disease penetrance, thereby influencing the risk of disease in individuals with inherited pathogenic variants. Manhattan plot of genome-wide heart failure associations adapted from REF., CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/).

Fig. 4 |. Connecting biology with data to guide precision care for heart failure.

a | Novel genetic discoveries from genome-wide association studies (GWAS) have identified genes in which common variants contribute to the risk of heart failure^,,. b | Pharmacogenomic studies have identified genetic marks that influence the clinical response to heart failure therapies^,–,. c | Proteomic studies have identified biological pathways that modulate the risk of heart failure and the response to therapies^–. d | Characterizing an individual’s heart failure biology from genetics, pharmacogenomics and proteomics can be combined with characterization derived from machine learning of clinical data to guide precision treatment of heart failure on the basis of the underlying causal biology. For example, Hedman and colleagues developed a model clustering algorithm based on echocardiographic and clinical laboratory variables in 320 outpatients with heart failure with preserved ejection fraction and identified six distinct phenogroups that had differential clinical outcomes and plasma levels of proteomic markers. For each variable, the population mean and standard deviation or the percentage with 95% confidence intervals is shown. The circles are sized according to the absolute Z-score and coloured according to a priori knowledge on heart failure, with red (increase) representing more severe heart failure and/or worse prognosis and blue (decrease) representing less severe heart failure and/or better prognosis. The lower graph shows Kaplan–Meier curves during 1,000 days of follow-up for each of the six phenogroups. In another example, Cikes and colleagues analysed differential responses to clinical therapy based on machine learning-derived phenogroups. An unsupervised machine learning algorithm was used to categorize participants in the MADIT-CRT trial on the basis of their clinical parameters, biomarkers and left ventricular (LV) volume, revealing four distinct phenogroups. Patients in phenogroups 1 and 3 had a beneficial response to cardiac resynchronization therapy–defibrillator (CRT-D) treatment compared with their response to implantable cardioverter–defibrillator (ICD) therapy only, and the response was much greater than in phenogroups 2 and 4 (Kaplan–Meier estimates of the probability of survival free from heart failure are shown for phenogroups 1 and 2 only). CKD, chronic kidney disease; DBP, diastolic blood pressure; EDV, end-diastolic volume; E/e′, ratio between early mitral inflow velocity and mitral annular early diastolic velocity; eGFR, estimated glomerular filtration rate; ESV, end-systolic volume; MKL, multiple kernel learning; WBC, white blood cell. Part d (left-hand graphs) adapted by permission from BMJ Publishing Group Limited from REF., Heart, Hedman, A. K. et al. 106, 342–349 (2020). Part d (right-hand graphs) adapted with permission from REF., Wiley.

Fig. 5 |. Precision medicine in heart failure.

a | The current model for the management of heart failure (HF) with reduced ejection fraction (HFrEF) is focused on initiation of medical therapies at the time of clinical onset of HF, with an emphasis on the initiation of β-blockers, an angiotensin-receptor blocker–neprilysin inhibitor (ARNI) and spironolactone, and recently on the early initiation of a sodium–glucose cotransporter 2 inhibitor (SGLT2i). b | Precision medicine is beginning to be incorporated into HF therapy. In patients with suspected Mendelian cardiovascular disease, a full three-generation family history is taken and genetic evaluation with counselling and sequencing is performed, often based on a gene panel populated with genes known to be associated with cardiomyopathy. Genetic diagnoses can lead to clear changes in treatment plans for particular conditions (such as hypertrophic cardiomyopathy (HCM), Fabry disease, transthyretin-associated amyloidosis or sarcoidosis) and has important implications for families. c | We propose a model for the future of precision medicine in patients with HFrEF. The earlier evaluation of the polygenic risk of HF will inform a physician to address modifiable risk factors. At the clinical onset of HF, the integration of biological causality through genetics, pharmacogenomics and proteomics with electronic health record data through machine learning algorithms can guide HF therapies. The ongoing evaluation of biological and clinical data can be used to predict treatment response and guide transition to advanced therapies. The gap between our current model and idealized models of precision medicine in HF is large. Bridging this gap must be evidence-based and be built on the evaluation of massive genetic, proteomic and electronic health record data sets from patients with real-world outcomes and from randomized controlled trials. HFpEF, heart failure with preserved ejection fraction; ICD, implantable cardioverter–defibrillator; LV, left ventricular.