Analysis of PTGS1 and PEAR1 Polymorphisms for Aspirin Resistance Prediction in Patients with Cerebral Infarction

Introduction This study aimed to evaluate the predictive potential of prostaglandin-endoperoxide synthase 1 ( PTGS1 ) and platelet endothelial aggregation receptor-1 ( PEAR1 ) gene polymorphisms, in combination with biochemical and demographic factors, for aspirin resistance (AR) and recurrence with cerebral infarction (CI) in Chinese patients. Methods A retrospective study was conducted on 280 patients diagnosed with CI and admitted to the Department of Neurology, Affiliated Hospital of Beihua University, between January and December 2021. Based on follow-up via telephone, patients were categorized into recurrent (n = 97) and non-recurrent (n = 183) groups. Clinical data, including age, sex, smoking and alcohol history, presence of coronary heart disease, diabetes, and hypertension, were collected. Laboratory indices measured upon admission included hemoglobin, triglycerides, total cholesterol, uric acid, and homocysteine (HCY) levels. Genotyping for PEAR1 and PTGS1 was performed. Univariate and multivariate logistic regression analyses were conducted to identify independent predictors of recurrence. A combined prediction model was developed, and receiver operating characteristic (ROC) curves were used to evaluate predictive performance. Results Significant differences were observed between the recurrent and non-recurrent groups in the distribution of PEAR1 and PTGS1 genotypes, the presence of hypertension, and levels of HCY ( p < 0.05). Multivariate logistic regression identified PEAR1 and PTGS1 polymorphisms, as well as elevated HCY, as independent predictors of cerebral infarction recurrence ( p < 0.05). ROC analysis demonstrated the following area under the curve (AUC) values for predicting recurrence: PEAR1 = 0.529, PTGS1 = 0.642, HCY = 0.696, and the combined prediction model = 0.721. The combined model showed significantly improved predictive value compared to individual markers ( p < 0.05). Conclusion PEAR1 and PTGS1 gene polymorphisms, when integrated with biochemical markers, such as HCY, could effectively predict the risk of recurrence in patients with cerebral infarction. The combined model has offered superior predictive value, supporting its potential clinical utility in guiding antiplatelet therapy for individualized risk stratification.

Authors

Institutions

Publication Details

Journal
The Open Medicinal Chemistry Journal
Published
2025-11-11
DOI
https://doi.org/10.2174/0118741045412609251014072341
Primary Topic
Antiplatelet Therapy and Cardiovascular Diseases
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Analysis of PTGS1 and PEAR1 Polymorphisms for Aspirin Resistance Prediction in Patients with Cerebral Infarction

Mohd Hijaz Mohd Sani, Yu Ning, Gao Yan, Rhanye Mac Guad
The Open Medicinal Chemistry Journal
Antiplatelet Therapy and Cardiovascular Diseases
article

Analysis of PTGS1 and PEAR1 Polymorphisms for Aspirin Resistance Prediction in Patients with Cerebral Infarction

Mohd Hijaz Mohd Sani, Yu Ning, Gao Yan, Rhanye Mac Guad
article en

Abstract

Introduction This study aimed to evaluate the predictive potential of prostaglandin-endoperoxide synthase 1 ( PTGS1 ) and platelet endothelial aggregation receptor-1 ( PEAR1 ) gene polymorphisms, in combination with biochemical and demographic factors, for aspirin resistance (AR) and recurrence with cerebral infarction (CI) in Chinese patients. Methods A retrospective study was conducted on 280 patients diagnosed with CI and admitted to the Department of Neurology, Affiliated Hospital of Beihua University, between January and December 2021. Based on follow-up via telephone, patients were categorized into recurrent (n = 97) and non-recurrent (n = 183) groups. Clinical data, including age, sex, smoking and alcohol history, presence of coronary heart disease, diabetes, and hypertension, were collected. Laboratory indices measured upon admission included hemoglobin, triglycerides, total cholesterol, uric acid, and homocysteine (HCY) levels. Genotyping for PEAR1 and PTGS1 was performed. Univariate and multivariate logistic regression analyses were conducted to identify independent predictors of recurrence. A combined prediction model was developed, and receiver operating characteristic (ROC) curves were used to evaluate predictive performance. Results Significant differences were observed between the recurrent and non-recurrent groups in the distribution of PEAR1 and PTGS1 genotypes, the presence of hypertension, and levels of HCY ( p < 0.05). Multivariate logistic regression identified PEAR1 and PTGS1 polymorphisms, as well as elevated HCY, as independent predictors of cerebral infarction recurrence ( p < 0.05). ROC analysis demonstrated the following area under the curve (AUC) values for predicting recurrence: PEAR1 = 0.529, PTGS1 = 0.642, HCY = 0.696, and the combined prediction model = 0.721. The combined model showed significantly improved predictive value compared to individual markers ( p < 0.05). Conclusion PEAR1 and PTGS1 gene polymorphisms, when integrated with biochemical markers, such as HCY, could effectively predict the risk of recurrence in patients with cerebral infarction. The combined model has offered superior predictive value, supporting its potential clinical utility in guiding antiplatelet therapy for individualized risk stratification.

The Open Medicinal Chemistry JournalVol. 19(1)
Beihua University (CN), Universiti of Malaysia Sabah (MY)
Beihua University
Openalex Percentile: Top 49%
Antiplatelet Therapy and Cardiovascular Diseases
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.