Association of CYP2C19 and GP1BA Genetic Variants With Antiplatelet Efficacy, and Prognosis in Patients With Acute Cerebral Infarction, and the Development of a Prognostic Risk Nomogram Model
Year: 2025
Type: article
Study design: Observational Study
Abstract: Aims/Background The efficacy of antiplatelet therapy exhibits interindividual variability, highlighting the need to identify underlying factors contributing to this heterogeneity in clinical settings. Increasing clinical attention has been directed toward genetic polymorphisms, aiming to optimise antiplatelet therapy based on patient genotype to enhance treatment efficacy and improve prognosis. This study aimed to analyse the relationship between cytochrome P450 2C19 (CYP2C19) and glycoprotein 1b alpha subunit gene (GP1BA) polymorphism and the efficacy of antiplatelet therapy and prognosis in patients with acute cerebral infarction (ACI). Methods A total of 200 ACI patients treated at First Affiliated Hospital of Bengbu Medical University between January 2021 and May 2024 were enrolled. The distributions of CYP2C19 and GP1BA gene polymorphisms were determined. Differences in platelet inhibition rates among patients with various CYP2C19 and GP1BA genotypes were compared. Clinical characteristics and genotype distributions were analysed between patients with varying prognoses. A nomogram model was constructed to predict prognosis. Results Among the CYP2C19 genotypes, 84 patients were classified as fast metabolizers, 82 as intermediate metabolizers, and 34 as slow metabolizers. For GP1BA, 94 patients had the CC genotype, 78 had CT, and 28 had TT. The platelet inhibition rate in slow metabolizers was (50.12 ± 13.32) %, markedly lower than in fast and intermediate metabolizers (p < 0.05). Among GP1BA genotypes, the platelet inhibition rate in CC type patients was (55.30 ± 9.92) %, significantly lower than in CT and TT types (p < 0.05). Patients with poor prognosis had a mean age of (65.59 ± 9.92) years and a baseline National Institutes of Health Stroke Scale (NIHSS) score of (14.50 ± 2.02), both significantly higher than those in the good prognosis group (p < 0.05). The proportion of patients with diabetes in the poor prognosis group was 43.75%, significantly higher than in the good prognosis group (p < 0.05). Significant differences in CYP2C19 and GP1BA genotype distributions were observed between patients with poor and good prognoses (p < 0.05). Logistic regression analysis identified age, NIHSS score at admission, diabetes, and both CYP2C19 and GP1BA genotypes as independent factors influencing poor prognosis (p < 0.05). The nomogram model constructed for prognosis prediction showed good performance, with the area under the curve (AUC) of 0.815 (95% confidence interval [CI]: 0.737–0.894, p < 0.05). The model demonstrated a sensitivity of 87.5% and a specificity of 65.90%. Conclusion CYP2C19 and GP1BA polymorphisms are related to the efficacy of antiplatelet therapy and are influencing factors for prognosis in ACI patients. The constructed nomogram model demonstrates good predictive value for clinical outcomes.
Institution: First Affiliated Hospital of Bengbu Medical College
Language: English
Subfield: Cardiology and Cardiovascular Medicine
Field: Medicine
Domain: Health Sciences
Open Access status: hybrid
Funder: Bengbu Medical College
OpenAlex ID: W4415997566