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AimsIn a retrospective analysis of dal-Outcomes, the effect of dalcetrapib on cardiovascular events was influenced by an adenylate cyclase type 9 (ADCY9) gene polymorphism. The dal-GenE study was conducted to test this pharmacogenetic hypothesis.Methods and resultsdal-GenE was a double-blind trial in patients with an acute coronary syndrome within 1-3 months and the AA genotype at variant rs1967309 in the ADCY9 gene. A total of 6147 patients were randomly assigned to receive dalcetrapib 600 mg or placebo daily. The primary endpoint was the time from randomization to first occurrence of cardiovascular death, resuscitated cardiac arrest, non-fatal myocardial infarction, or non-fatal stroke. After a median follow-up of 39.9 months, the primary endpoint occurred in 292 (9.5%) of 3071 patients in the dalcetrapib group and 327 (10.6%) of 3076 patients in the placebo group [hazard ratio 0.88; 95% confidence interval (CI) 0.75-1.03; P = 0.12]. The hazard ratios for the components of the primary endpoint were 0.79 (95% CI 0.65-0.96) for myocardial infarction, 0.92 (95% CI 0.64-1.33) for stroke, 1.21 (95% CI 0.91-1.60) for death from cardiovascular causes, and 2.33 (95% CI 0.60-9.02) for resuscitated cardiac arrest. In a pre-specified on-treatment sensitivity analysis, the primary endpoint event rate was 7.8% (236/3015) in the dalcetrapib group and 9.3% (282/3031) in the placebo group (hazard ratio 0.83; 95% CI 0.70-0.98).ConclusionDalcetrapib did not significantly reduce the risk of occurrence of the primary endpoint of ischaemic cardiovascular events at end of study. A new trial would be needed to test the pharmacogenetic hypothesis that dalcetrapib improves the prognosis of patients with the AA genotype.Clinical trial registrationTrial registration dal-GenE ClinicalTrials.gov Identifier: NCT02525939.

AimsThe COLchicine Cardiovascular Outcomes Trial (COLCOT) demonstrated the benefits of targeting inflammation after myocardial infarction (MI). We aimed to determine whether time-to-treatment initiation (TTI) influences the beneficial impact of colchicine.Methods and resultsIn COLCOT, patients were randomly assigned to receive colchicine or placebo within 30 days post-MI. Time-to-treatment initiation was defined as the length of time between the index MI and the initiation of study medication. The primary efficacy endpoint was a composite of cardiovascular death, resuscitated cardiac arrest, MI, stroke, or urgent hospitalization for angina requiring coronary revascularization. The relationship between endpoints and various TTI (8 days) was examined using multivariable Cox regression models. Amongst the 4661 patients included in this analysis, there were 1193, 720, and 2748 patients, respectively, in the three TTI strata. After a median follow-up of 22.7 months, there was a significant reduction in the incidence of the primary endpoint for patients in whom colchicine was initiated < Day 3 compared with placebo [hazard ratios (HR) = 0.52, 95% confidence intervals (CI) 0.32-0.84], in contrast to patients in whom colchicine was initiated between Days 4 and 7 (HR = 0.96, 95% CI 0.53-1.75) or > Day 8 (HR = 0.82, 95% CI 0.61-1.11). The beneficial effects of early initiation of colchicine were also demonstrated for urgent hospitalization for angina requiring revascularization (HR = 0.35), all coronary revascularization (HR = 0.63), and the composite of cardiovascular death, resuscitated cardiac arrest, MI, or stroke (HR = 0.55, all P

Background and Aims Deep learning applied to electrocardiograms (ECG-AI) is an emerging approach for predicting atrial fibrillation or flutter (AF). This study introduces an ECG-AI model developed and tested at a tertiary cardiac centre, comparing its performance with clinical models and AF polygenic score (PGS). Methods Electrocardiograms in sinus rhythm from the Montreal Heart Institute were analysed, excluding those from patients with pre-existing AF. The primary outcome was incident AF at 5 years. An ECG-AI model was developed by splitting patients into non-overlapping data sets: 70% for training, 10% for validation, and 20% for testing. The performance of ECG-AI, clinical models, and PGS was assessed in the test data set. The ECG-AI model was externally validated in the Medical Information Mart for Intensive Care-IV (MIMIC-IV) hospital data set. Results A total of 669 782 ECGs from 145 323 patients were included. Mean age was 61 ± 15 years, and 58% were male. The primary outcome was observed in 15% of patients, and the ECG-AI model showed an area under the receiver operating characteristic (AUC-ROC) curve of.78. In time-to-event analysis including the first ECG, ECG-AI inference of high risk identified 26% of the population with a 4.3-fold increased risk of incident AF (95% confidence interval: 4.02–4.57). In a subgroup analysis of 2301 patients, ECG-AI outperformed CHARGE-AF (AUC-ROC =.62) and PGS (AUC-ROC =.59). Adding PGS and CHARGE-AF to ECG-AI improved goodness of fit (likelihood ratio test P <.001), with minimal changes to the AUC-ROC (.76–.77). In the external validation cohort (mean age 59 ± 18 years, 47% male, median follow-up 1.1 year), ECG-AI model performance remained consistent (AUC-ROC =.77). Conclusions ECG-AI provides an accurate tool to predict new-onset AF in a tertiary cardiac centre, surpassing clinical and PGS. [ABSTRACT FROM AUTHOR]

Aims: Inflammation plays an important role in cardiovascular disease (CVD) development. The NOD-like receptor protein-3 (NLRP3) inflammasome contributes to the development of atherosclerosis in animal models. Components of the NLRP3 inflammasome pathway such as interleukin-1β can therapeutically be targeted. Associations of genetically determined inflammasome-mediated systemic inflammation with CVD and mortality in humans are unknown., Methods and Results: We explored the association of genetic NLRP3 variants with prevalent CVD and cardiovascular mortality in 538 167 subjects on the individual participant level in an explorative gene-centric approach without performing multiple testing. Functional relevance of single-nucleotide polymorphisms on NLRP3 inflammasome activation has been evaluated in monocyte-enriched peripheral blood mononuclear cells (PBMCs). Genetic analyses identified the highly prevalent (minor allele frequency 39.9%) intronic NLRP3 variant rs10754555 to affect NLRP3 gene expression. rs10754555 carriers showed significantly higher C-reactive protein and serum amyloid A plasma levels. Carriers of the G allele showed higher NLRP3 inflammasome activation in isolated human PBMCs. In carriers of the rs10754555 variant, the prevalence of coronary artery disease was significantly higher as compared to non-carriers with a significant interaction between rs10754555 and age. Importantly, rs10754555 carriers had significantly higher risk for cardiovascular mortality during follow-up. Inflammasome inducers (e.g. urate, triglycerides, apolipoprotein C3) modulated the association between rs10754555 and mortality., Conclusion: The NLRP3 intronic variant rs10754555 is associated with increased systemic inflammation, inflammasome activation, prevalent coronary artery disease, and mortality. This study provides evidence for a substantial role of genetically driven systemic inflammation in CVD and highlights the NLRP3 inflammasome as a therapeutic target., (Published on behalf of the European Society of Cardiology. All rights reserved. © The Author(s) 2021. For permissions, please email: journals.permissions@oup.com.)