Individualised therapy of angiotensin converting enzyme (ACE) inhibitors in stable coronary artery disease: overview of the primary results of the PERindopril GENEtic association (PERGENE) study

In patients with stable coronary artery disease (CAD) without overt heart failure, ACE inhibitors are among the most commonly used drugs as these agents have been proven effective in reducing the risk of cardiovascular events. Considerable individual variations in the blood pressure response to ACE inhibitors are observed and as such heterogeneity in clinical treatment effect would be likely as well. Assessing the consistency of treatment benefit is essential for the rational and cost-effective prescription of ACE inhibitors. Information on heterogeneities in treatment effect between subgroups of patients could be used to develop an evidence-based guidance for the installation of ACE-inhibitor therapy. Obviously, therapy should only be applied in those patients who most likely will benefit. Attempts to develop such treatment guidance by using clinical characteristics have been unsuccessful. No heterogeneity in risk reduction by ACE inhibitors has been observed in relation to relevant clinical characteristics. A new approach to such ‘guided-therapy’ could be to integrate more patient-specific characteristics such as the patients’ genetic information. If proven feasible, pharmacogenetic profiling could optimise patients’ benefit of treatment and reduce unnecessary treatment of patients. Cardiovascular pharmacogenetic research of ACE inhibitors in coronary artery disease patients is in a formative stage and studies are limited. The PERGENE study is a large pharmacogenetic substudy of the EUROPA trial, aimed to assess the achievability of pharmacogenetic profiling. We provide an overview of the main results of the PERGENE study in terms of the genetic determinants of treatment benefit and blood pressure response. The main results of the PERGENE study show a pharmacogenetic profile related to the treatment benefit of perindopril identifying responders and non-responders to treatment

Angiotensin-converting enzyme inhibitors reduce mortality in hypertension: a meta-analysis of randomized clinical trials of reninangiotensinaldosterone system inhibitors involving 158 998 patients

AimsRenin-Angiotensin-Aldosterone system (RAAS) inhibitors are well established for the reduction in cardiovascular morbidity, but their impact on all-cause mortality in hypertensive patients is uncertain. Our objective was to analyse the effects of RAAS inhibitors as a class of drugs, as well as of angiotensin-converting enzyme (ACE) inhibitors and AT1 receptor blockers (ARBs) separately, on all-cause mortality. Methods and resultsWe performed a pooled analysis of 20 cardiovascular morbiditymortality trials. In each trial at least two-thirds of the patients had to be diagnosed with hypertension, according to the trial-specific definition, and randomized to treatment with an RAAS inhibitor or control treatment. The cohort included 158 998 patients (71 401 RAAS inhibitor; 87 597 control). The incidence of all-cause death was 20.9 and 23.3 per 1000 patient-years in patients randomized to RAAS inhibition and controls, respectively. Overall, RAAS inhibition was associated with a 5 reduction in all-cause mortality (HR: 0.95, 95 CI: 0.911.00, P = 0.032), and a 7 reduction in cardiovascular mortality (HR: 0.93, 95 CI: 0.880.99, P = 0.018). The observed treatment effect resulted entirely from the class of ACE inhibitors, which were associated with a significant 10 reduction in all-cause mortality (HR: 0.90, 95 CI: 0.840.97, P = 0.004), whereas no mortality reduction could be demonstrated with ARB treatment (HR: 0.99, 95 CI: 0.941.04, P = 0.683). This difference in treatment effect between ACE inhibitors and ARBs on all-cause mortality was statistically significant (P-value for heterogeneity 0.036). ConclusionIn patients with hypertension, treatment with an ACE inhibitor results in a significant further reduction in all-cause mortality. Because of the high prevalence of hypertension, the widespread use of ACE inhibitors may result in an important gain in lives saved

Individualized Angiotensin‐Converting Enzyme (ACE)‐Inhibitor Therapy in Stable Coronary Artery Disease Based on Clinical and Pharmacogenetic Determinants: The PERindopril GENEtic (PERGENE) Risk Model

Patients with stable coronary artery disease (CAD) constitute a heterogeneous group in which the treatment benefits by angiotensin-converting enzyme (ACE)-inhibitor therapy vary between individuals. Our objective was to integrate clinical and pharmacogenetic determinants in an ultimate combined risk prediction model.Clinical, genetic, and outcomes data were used from 8726 stable CAD patients participating in the EUROPA/PERGENE trial of perindopril versus placebo. Multivariable analysis of phenotype data resulted in a clinical risk score (range, 0-21 points). Three single-nucleotide polymorphisms (rs275651 and rs5182 in the angiotensin-II type I-receptor gene and rs12050217 in the bradykinin type I-receptor gene) were used to construct a pharmacogenetic risk score (PGXscore; range, 0-6 points). Seven hundred eighty-five patients (9.0%) experienced the primary endpoint of cardiovascular mortality, nonfatal myocardial infarction or resuscitated cardiac arrest, during 4.2 years of follow-up. Absolute risk reductions ranged from 1.2% to 7.5% in the 73.5% of patients with PGXscore of 0 to 2. As a consequence, estimated annual numbers needed to treat ranged from as low as 29 (clinical risk score ≥10 and PGXscore of 0) to 521 (clinical risk score ≤6 and PGXscore of 2). Furthermore, our data suggest that long-term perindopril prescription in patients with a PGXscore of 0 to 2 is cost-effective.Both baseline clinical phenotype, as well as genotype determine the efficacy of widely prescribed ACE inhibition in stable CAD. Integration of clinical and pharmacogenetic determinants in a combined risk prediction model demonstrated a very wide range of gradients of absolute treatment benefit

Discovering Distinct Phenotypical Clusters in Heart Failure Across the Ejection Fraction Spectrum: a Systematic Review

Review Purpose: This systematic review aims to summarise clustering studies in heart failure (HF) and guide future clinical trial design and implementation in routine clinical practice. Findings: 34 studies were identified (n = 19 in HF with preserved ejection fraction (HFpEF)). There was significant heterogeneity invariables and techniques used. However, 149/165 described clusters could be assigned to one of nine phenotypes: 1) young, low comorbidity burden; 2) metabolic; 3) cardio-renal; 4) atrial fibrillation (AF); 5) elderly female AF; 6) hypertensive-comorbidity; 7) ischaemic-male; 8) valvular disease; and 9) devices. There was room for improvement on important methodological topics for all clustering studies such as external validation and transparency of the modelling process. Summary: The large overlap between the phenotypes of the clustering studies shows that clustering is a robust approach for discovering clinically distinct phenotypes. However, future studies should invest in a phenotype model that can be implemented in routine clinical practice and future clinical trial design. Graphical Abstract: HF = heart failure, EF = ejection fraction, HFpEF = heart failure with preserved ejection fraction, HFrEF = heart failure with reduced ejection fraction, CKD = chronic kidney disease, AF = atrial fibrillation, IHD = ischaemic heart disease, CAD = coronary artery disease, ICD = implantable cardioverter-defibrillator, CRT = cardiac resynchronization therapy, NT-proBNP = N-terminal pro b-type natriuretic peptide, BMI = Body Mass Index, COPD = Chronic obstructive pulmonary disease

Embedding routine health care data in clinical trials: with great power comes great responsibility

Randomised clinical trials (RCTs) are vital for medical progress. Unfortunately, ‘traditional’ RCTs are expensive and inherently slow. Moreover, their generalisability has been questioned. There is considerable overlap in routine health care data (RHCD) and trial-specific data. Therefore, integration of RHCD in an RCT has great potential, as it would reduce the effort and costs required to collect data, thereby overcoming some of the major downsides of a traditional RCT. However, use of RHCD comes with other challenges, such as privacy issues, as well as technical and practical barriers. Here, we give a current overview of related initiatives on national cardiovascular registries (Netherlands Heart Registration, Heart4Data), showcasing the interrelationships between and the relevance of the different registries for the practicing physician. We then discuss the benefits and limitations of RHCD use in the setting of a pragmatic RCT from a cardiovascular perspective, illustrated by a case study in heart failure

HFrEF subphenotypes based on 4210 repeatedly measured circulating proteins are driven by different biological mechanisms

BACKGROUND: HFrEF is a heterogenous condition with high mortality. We used serial assessments of 4210 circulating proteins to identify distinct novel protein-based HFrEF subphenotypes and to investigate underlying dynamic biological mechanisms. Herewith we aimed to gain pathophysiological insights and fuel opportunities for personalised treatment. METHODS: In 382 patients, we performed trimonthly blood sampling during a median follow-up of 2.1 [IQR:1.1–2.6] years. We selected all baseline samples and two samples closest to the primary endpoint (PEP; composite of cardiovascular mortality, HF hospitalization, LVAD implantation, and heart transplantation) or censoring, and applied an aptamer-based multiplex proteomic approach. Using unsupervised machine learning methods, we derived clusters from 4210 repeatedly measured proteomic biomarkers. Sets of proteins that drove cluster allocation were analysed via an enrichment analysis. Differences in clinical characteristics and PEP occurrence were evaluated. FINDINGS: We identified four subphenotypes with different protein profiles, prognosis and clinical characteristics, including age (median [IQR] for subphenotypes 1–4, respectively:70 [64, 76], 68 [60, 79], 57 [47, 65], 59 [56, 66]years), EF (30 [26, 36], 26 [20, 38], 26 [22, 32], 33 [28, 37]%), and chronic renal failure (45%, 65%, 36%, 37%). Subphenotype allocation was driven by subsets of proteins associated with various biological functions, such as oxidative stress, inflammation and extracellular matrix organisation. Clinical characteristics of the subphenotypes were aligned with these associations. Subphenotypes 2 and 3 had the worst prognosis compared to subphenotype 1 (adjHR (95%CI):3.43 (1.76–6.69), and 2.88 (1.37–6.03), respectively). INTERPRETATION: Four circulating-protein based subphenotypes are present in HFrEF, which are driven by varying combinations of protein subsets, and have different clinical characteristics and prognosis. CLINICAL TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT01851538 https://clinicaltrials.gov/ct2/show/NCT01851538. Funding: EU/ EFPIA IMI2JU BigData@Heart grant n° 116074, Jaap Schouten Foundation and Noordwest Academie

Clinical profile and contemporary management of patients with heart failure with preserved ejection fraction: results from the CHECK-HF registry

Background: Clinical management of heart failure with preserved ejection fraction (HFpEF) centres on treating comorbidities and is likely to vary between countries. Thus, to provide insight into the current management of HFpEF, studies from multiple countries are required. We evaluated the clinical profiles and current management of patients with HFpEF in the Netherlands. Methods: We included 2153 patients with HFpEF (defined as a left ventricular ejection fraction ≥ 50%) from the CHECK-HF registry, which included patients from 2013 to 2016. Results: Median age was 77 (IQR 15) years, 55% were women and the most frequent comorbidities were hypertension (51%), renal insufficiency (45%) and atrial fibrillation (AF, 38%). Patients between 65 and 80 years and those over 80 years had on average more comorbidities (up to 64% and 74%, respectively, with two or more comorbidities) than patients younger than 65 years (38% with two or more comorbidities, p-value < 0.001). Although no specific drugs are available for HFpEF, treating comorbidities is advised. Beta-blockers were most frequently prescribed (78%), followed by loop diuretics (74%), renin-angiotensin system (RAS) inhibitors (67%) and mineralocorticoid receptor antagonists (MRAs, 39%). Strongest predictors for loop-diuretic use were older age, higher New York Heart Association class and AF. Conclusion: The medical HFpEF profile is determined by the underlying comorbidities, sex and age. Comorbidities are highly prevalent in HFpEF patients, especially in elderly HFpEF patients. Despite the lack of evidence, many HFpEF patients receive regular beta-blockers, RAS inhibitors and MRAs, often for the treatment of comorbidities