The Clinical Implementation of CYP2C19 Genotyping in Patients with an Acute Coronary Syndrome:Insights From the FORCE-ACS Registry

BACKGROUND: Guidelines recommend prasugrel or ticagrelor for acute coronary syndrome (ACS) patients. However, these P2Y12 inhibitors increase bleeding risk compared to clopidogrel. Although genotype-guided P2Y12-inhibitor selection has been shown to reduce bleeding risk, data on its clinical implementation is lacking. METHODS: The study included ACS patients receiving genotype-guided antiplatelet therapy, utilising either a point-of-care (POC) device or laboratory-based testing. We aimed to collect qualitative and quantitative data on genotyping, eligibility for de-escalation, physician adherence to genotype results, time to de-escalation and cost reduction. RESULTS: Of the 1,530 patients included in the ACS registry from 2021 to 2023, 738 ACS patients treated with ticagrelor received a CYP2C19 genotype test. The median turnover time of genotyping was 6.3 hours (interquartile range [IQR], 3.2-16.7), with 82.3% of the genotyping results known within 24 hours after admission. POC genotyping exhibited significantly shorter turnaround times compared to laboratory-based testing (with respective medians of 5.7 vs 47.8 hours; P &lt; .001). Of the genotyped patients, 81.7% were eligible for de-escalation which was carried out within 24 hours in 70.9% and within 48 h in 93.0%. The time to de-escalation was significantly shorter using POC (25.4 hours) compared to laboratory-based testing (58.9 hours; P &lt; .001). Implementing this strategy led to a reduction of €211,150.50 in medication costs. CONCLUSIONS: CYP2C19 genotype-guided-de-escalation in an all-comers ACS population is feasible. POC genotyping leads to shorter turnaround times and quicker de-escalation. Time to de-escalation from ticagrelor to clopidogrel in noncarriers was short, with high physician adherence to genotype results.</p

Female chromosome X mosaicism is age-related and preferentially affects the inactivated X chromosome

To investigate large structural clonal mosaicism of chromosome X, we analysed the SNP microarray intensity data of 38,303 women from cancer genome-wide association studies (20,878 cases and 17,425 controls) and detected 124 mosaic X events42Mb in 97 (0.25%) women. Here we show rates for X-chromosome mosaicism are four times higher than mean autosomal rates; X mosaic events more often include the entire chromosome and participants with X events more likely harbour autosomal mosaic events. X mosaicism frequency increases with age (0.11% in 50-year olds; 0.45% in 75-year olds), as reported for Y and autosomes. Methylation array analyses of 33 women with X mosaicism indicate events preferentially involve the inactive X chromosome. Our results provide further evidence that the sex chromosomes undergo mosaic events more frequently than autosomes, which could have implications for understanding the underlying mechanisms of mosaic events and their possible contribution to risk for chronic diseases

Integrated analysis of environmental and genetic influences on cord blood DNA methylation in new-borns

Epigenetic processes, including DNA methylation (DNAm), are among the mechanisms allowing integration of genetic and environmental factors to shape cellular function. While many studies have investigated either environmental or genetic contributions to DNAm, few have assessed their integrated effects. Here we examine the relative contributions of prenatal environmental factors and genotype on DNA methylation in neonatal blood at variably methylated regions (VMRs) in 4 independent cohorts (overall n = 2365). We use Akaike’s information criterion to test which factors best explain variability of methylation in the cohort-specific VMRs: several prenatal environmental factors (E), genotypes in cis (G), or their additive (G + E) or interaction (GxE) effects. Genetic and environmental factors in combination best explain DNAm at the majority of VMRs. The CpGs best explained by either G, G + E or GxE are functionally distinct. The enrichment of genetic variants from GxE models in GWAS for complex disorders supports their importance for disease risk

Effects of CYP3A4*22 and CYP3A5 on clinical outcome in patients treated with ticagrelor for ST-segment elevation myocardial infarction: POPular Genetics sub-study

Aims: To determine the clinical efficacy, adverse events and side-effect dyspnea of CYP3A4*22 and CYP3A5 expressor status in ticagrelor treated patients. Methods and results: Ticagrelor treated patients from the POPular Genetics randomized controlled trial were genotyped for CYP3A4*22 and CYP3A5*3 alleles. Patients were divided based on their genotype. In total 1,281 patients with ST-segment elevation myocardial infarction (STEMI) were included. CYP3A4*22 carriers (n = 152) versus CYP3A4*22 non-carrier status (n = 1,129) were not found to have a significant correlation with the primary thrombotic endpoint: cardiovascular death, myocardial infarction, definite stent thrombosis and stroke [1.3% vs. 2.5%, adjusted hazard ratio 1.81 (0.43–7.62) p = 0.42], or the primary bleeding endpoint: PLATO major and minor bleeding [13.2% vs. 11.3%, adjusted hazard ratio 0.93 (0.58–1.50) p = 0.77]. Among the CYP3A4*1/*1 patients, CYP3A5 expressors (n = 196) versus non-expressors (n = 926) did not show a significant difference for the primary thrombotic [2.6% vs. 2.5%, adjusted hazard ratio 1.03 (0.39–2.71) p = 0.95], or the primary bleeding endpoint [12.8% vs. 10.9%, adjusted hazard ratio 1.13 (0.73–1.76) p = 0.58]. With respect to dyspnea, no significant difference was observed between CYP3A4*22 carriers versus CYP3A4*22 non-carriers [44.0% vs. 45.0%, odds ratio 1.04 (0.45–2.42) p = 0.93], or in the CYP3A4*1/*1 group, CYP3A5 expressors versus CYP3A5 non-expressors [35.3% vs. 47.8%, odds ratio 0.60 (0.27–1.30) p = 0.20]. Conclusion: In STEMI patients treated with ticagrelor, neither the CYP3A4*22 carriers, nor the CYP3A5 expressor status had a statistical significant effect on thrombotic and bleeding event rates nor on dyspnea. Clinical Trial Registration: ClinicalTrials.gov, identifier NCT01761786