Association of <i>CYP2C19</i> and <i>GP1BA</i> Genetic Variants With Antiplatelet Efficacy, and Prognosis in Patients With Acute Cerebral Infarction, and the Development of a Prognostic Risk Nomogram Model

<b>Aims/Background</b> 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 (<i>CYP2C19</i>) and glycoprotein 1b alpha subunit gene (<i>GP1BA</i>) polymorphism and the efficacy of antiplatelet therapy and prognosis in patients with acute cerebral infarction (ACI). <b>Methods</b> 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 <i>CYP2C19</i> and <i>GP1BA</i> gene polymorphisms were determined. Differences in platelet inhibition rates among patients with various <i>CYP2C19</i> and <i>GP1BA</i> genotypes were compared. Clinical characteristics and genotype distributions were analysed between patients with varying prognoses. A nomogram model was constructed to predict prognosis. <b>Results</b> Among the <i>CYP2C19</i> genotypes, 84 patients were classified as fast metabolizers, 82 as intermediate metabolizers, and 34 as slow metabolizers. For <i>GP1BA</i>, 94 patients had the <i>CC</i> genotype, 78 had <i>CT</i>, and 28 had <i>TT</i>. The platelet inhibition rate in slow metabolizers was (50.12 ± 13.32) %, markedly lower than in fast and intermediate metabolizers (<i>p</i> < 0.05). Among <i>GP1BA</i> genotypes, the platelet inhibition rate in <i>CC</i> type patients was (55.30 ± 9.92) %, significantly lower than in <i>CT</i> and <i>TT</i> types (<i>p</i> < 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 (<i>p</i> < 0.05). The proportion of patients with diabetes in the poor prognosis group was 43.75%, significantly higher than in the good prognosis group (<i>p</i> < 0.05). Significant differences in <i>CYP2C19</i> and <i>GP1BA</i> genotype distributions were observed between patients with poor and good prognoses (<i>p</i> < 0.05). Logistic regression analysis identified age, NIHSS score at admission, diabetes, and both <i>CYP2C19</i> and <i>GP1BA</i> genotypes as independent factors influencing poor prognosis (<i>p</i> < 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, <i>p</i> < 0.05). The model demonstrated a sensitivity of 87.5% and a specificity of 65.90%. <b>Conclusion</b> <i>CYP2C19</i> and <i>GP1BA</i> 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.