Correlations among different platelet aggregation pathways in a group of healthy volunteers.
Alejandro CarazoMarcel HrubšaLukáš KonečnýCatherine GunaseelanJaka FadraersadaPavel SkořepaMarkéta PaclíkováFrantišek MusilJana KarlíčkováLenka JavorskáKateřina MatoušováLenka Kujovská KrčmováAlena ŠmahelováVladimír BlahaPřemysl MladěnkaPublished in: Platelets (2024)
Platelet aggregation is a complicated process mediated by different signaling pathways. As the process is highly complex and apparently redundant, the relationships between these pathways are not yet fully known. The aim of this project was to study the interconnections among seven different aggregation pathways in a group of 53 generally healthy volunteers aged 20 to 66 years. Platelet aggregation was induced with thrombin receptor activating peptide 6 (TRAP), arachidonic acid (AA), platelet activating factor 16 (PAF), ADP, collagen, thromboxane A 2 analogue U46619 or ristocetin (platelet agglutination) ex vivo in fasting blood samples according to standardized timetable protocol. Additionally, some samples were pre-treated with known clinically used antiplatelet drugs (vorapaxar, ticagrelor or acetylsalicylic acid (ASA)). Significant correlations among all used inducers were detected (Pearson correlation coefficients (r P ): 0.3 to 0.85). Of all the triggers, AA showed to be the best predictor of the response to other inducers with r P ranging from 0.66 to 0.85. Interestingly, the antiplatelet response to ticagrelor strongly predicted the response to unrelated drug vorapaxar (r P = 0.71). Our results indicate that a response to one inducer can predict the response for other triggers or even to an antiplatelet drug. These data are useful for future testing but should be also confirmed in patients.
Keyphrases
- signaling pathway
- acute coronary syndrome
- percutaneous coronary intervention
- end stage renal disease
- drug induced
- randomized controlled trial
- chronic kidney disease
- st segment elevation myocardial infarction
- antiplatelet therapy
- type diabetes
- coronary artery disease
- st elevation myocardial infarction
- emergency department
- metabolic syndrome
- machine learning
- atrial fibrillation
- adipose tissue
- high glucose
- insulin resistance
- big data
- pi k akt
- oxidative stress
- blood pressure
- adverse drug
- cell proliferation
- endothelial cells
- blood glucose
- diabetic rats
- binding protein