Differential Impact of Clinical and Genetic Factors on High Platelet Reactivity in Patients with Coronary Artery Disease Treated with Clopidogrel and Prasugrel

Update on antithrombotic therapy and body mass: a clinical consensus statement of the European Society of Cardiology Working Group on Cardiovascular Pharmacotherapy and the European Society of Cardiology Working Group on Thrombosis

Obesity and underweight are a growing health problem worldwide and a challenge for clinicians concerning antithrombotic therapy, due to the associated risks of thrombosis and/or bleeding. This clinical consensus statement updates a previous one published in 2018, by reviewing the most recent evidence on antithrombotic drugs based on body size categories according to the World Health Organization classification. The document focuses mostly on individuals at the extremes of body weight, i.e. underweight and moderate-to-morbid obesity, who require antithrombotic drugs, according to current guidelines, for the treatment or prevention of cardiovascular diseases or venous thromboembolism. Managing antithrombotic therapy or thromboprophylaxis in these individuals is challenging, due to profound changes in body composition, metabolism and organ function, and altered drug pharmacokinetics and pharmacodynamics, as well as weak or no evidence from clinical trials. The document also includes artificial intelligence simulations derived from in silico pharmacokinetic/pharmacodynamic models, which can mimic the pharmacokinetic changes and help identify optimal regimens of antithrombotic drugs for severely underweight or severely obese individuals. Further, bariatric surgery in morbidly obese subjects is frequently performed worldwide. Bariatric surgery causes specific and additional changes in metabolism and gastrointestinal anatomy, depending on the type of the procedure, which can also impact the pharmacokinetics of antithrombotic drugs and their management. Based on existing literature, the document provides consensus statements on optimizing antithrombotic drug management for underweight and all classes of obese patients, while highlighting the current gaps in knowledge in these complex clinical settings, which require personalized medicine and precision pharmacology.

Effects of Body Mass Index and Body Weight on Plasma Concentration of Ticagrelor and Platelet Aggregation Rate in Patients with Unstable Angina in a Chinese Han Population

Background

The aim of this study was to investigate the impact of body mass index (BMI) and body weight on the concentrations of ticagrelor and the ticagrelor metabolite, AR-C124910XX, as well as the platelet aggregation rate (PAR) in a Chinese Han population with unstable angina (UA). Specifically, it focused on these parameters following the administration of dual antiplatelet therapy (DAPT) comprising aspirin and ticagrelor.

Methods

A total of 105 patients with UA were included in the study. Measurement of the platelet aggregation rate induced by adenosine diphosphate (PAR-ADP) was performed before, as well as 3 and 30 days after DAPT treatment. The plasma concentrations of ticagrelor and AR-C124910XX were detected at 3 and 30 days after DAPT treatment. We conducted correlation analyses to assess the effects of BMI and body weight on the concentrations of ticagrelor and AR-C124910XX, on PAR-ADP, and on the inhibition of platelet aggregation induced by adenosine diphosphate (IPA-ADP) at both 3 and 30 days after DAPT treatment.

Results

The BMI and body weight were positively correlated with baseline PAR-ADP (r = 0.205, p = 0.007; r = 0.122, p = 0.022). The PAR-ADP at 3 and 30 days after DAPT treatment were significantly lower than at baseline (61.56% ± 10.62%, 8.02% ± 7.52%, 12.90% ± 7.42%, p < 0.001). There was a negative correlation between body weight and the concentrations of ticagrelor and AR-C124910XX at 3 days following DAPT treatment (r = -0.276, p < 0.001; r = -0.337, p < 0.001). Additionally, BMI showed a similar negative correlation with the concentrations of ticagrelor and AR-C124910XX (r = -0.173, p = 0.009; r = -0.207, p = 0.002). At 30 days after treatment, both body weight and BMI were negatively correlated with ticagrelor (r = -0.256, p < 0.001; r = -0.162, p = 0.015) and its metabolite (r = -0.352, p < 0.001; r = -0.202, p = 0.002). Body weight was positively correlated with PAR-ADP (r = 0.171, p = 0.010) and negatively correlated with IPA-ADP (r = -0.163, p = 0.015) at 30 days after treatment. Similarly, BMI was positively correlated with PAR-ADP (r = 0.217, p = 0.001) and negatively correlated with IPA-ADP (r = -0.211, p = 0.001) at the same time point.

Conclusions

BMI and body weight are key factors influencing the pharmacokinetics and pharmacodynamics of ticagrelor in Chinese Han patients with UA following DAPT treatment that includes ticagrelor. Both BMI and body weight were positively correlated with PAR-ADP at baseline and 30 days after DAPT treatment.

Clinical Trial Registration

ChiCTR2100044938, https://www.chictr.org.cn/.

Differential Impact of Clinical Factors for Predicting High Platelet Reactivity on Clinical Outcomes in Acute Myocardial Infarction Patients Treated With Clopidogrel and Prasugrel

AIMS

Several scoring systems, including the ABCD-GENE and HHD-GENE scores incorporating clinical and genetic factors, have been developed to identify patients likely to have high platelet reactivity on P2Y12 inhibitors, leading to increased risks of ischemic events. However, genetic testing is not widely available in daily practice. We aimed to evaluate the differential impact of clinical factors in the scores on ischemic outcomes in patients treated with clopidogrel and prasugrel.

METHODS

This bi-center registry included 789 patients with acute myocardial infarction (MI) undergoing percutaneous coronary intervention and treated with either clopidogrel or prasugrel at discharge. The relations of the number of clinical factors included in the ABCD-GENE (age ≥ 75 years, body mass index ＞30 kg/m2, chronic kidney disease, and diabetes) and HHD-GENE (hypertension, hemodialysis, and diabetes) scores to the primary endpoint of major cardiovascular events after discharge, a composite of death, recurrent MI, and ischemic stroke, were evaluated.

RESULTS

The number of clinical factors in the ABCD-GENE score was not predictive of ischemic outcomes after discharge in patients treated with clopidogrel and/or prasugrel, while the increase in the number of clinical factors of the HHD-GENE score was associated with an increased risk of the primary endpoint in a stepwise manner in patients on a P2Y12 inhibitor.

CONCLUSIONS

Clinical factors listed in the HHD-GENE score may help stratify ischemic risks in patients with acute MI treated with clopidogrel and prasugrel, whereas risk stratification without genetic testing in patients treated with clopidogrel may be challenging.

Derivation of a Novel Scoring System Predicting High Platelet Reactivity on Prasugrel in Patients with Coronary Artery Disease

AIMS

High platelet reactivity (HPR) has been associated with an increased risk of thrombotic events in patients undergoing percutaneous coronary intervention. HPR has been well examined in patients treated with clopidogrel; however, HPR on prasugrel is poorly investigated.

METHODS

Four prospective studies were pooled, in which platelet reactivity on prasugrel was measured using VerifyNow assay; genotyping of CYP2C19 was also performed. Factors associated with HPR on prasugrel were identified using multivariable analysis to develop a risk prediction model.

RESULTS

In total, 180 patients were examined in this study, of whom 51 (28%) had HPR on prasugrel. The multivariable analysis indicated that hypertension, diabetes, hemodialysis, and the number of CYP2C19 loss-of-function (LOF) alleles are significant factors for HPR on prasugrel. These four factors were then incorporated to develop the HHD-GENE score. The receiver operating characteristic curve analysis showed that the HHD-GENE score predicted HPR on prasugrel (area under the curve (AUC) 0.74, best cutoff value 5, p＜0.001). With the best cutoff value, patients with the HHD-GENE score ≥ 5 had a significantly increased risk of HPR on prasugrel than their counterpart (50% vs. 18%, p＜0.001).

CONCLUSIONS

The HHD-GENE score consisting of hypertension, diabetes, hemodialysis, and CYP2C19 LOF alleles may be useful in identifying patients on prasugrel who are at high risk for HPR. External validation is needed to define the clinical utility of this novel scoring system.