Correlation between plasma Hcy and the degree of coronary artery calcification in the elderly
10.3969/j.issn.1673-9701.2025.20.005
- VernacularTitle:血浆Hcy与老年人冠状动脉钙化程度关系研究
- Author:
Jingyuan GAO
1
;
Qianqian PENG
;
Liming HAN
;
Yawen WU
;
Han YAN
;
Jingwei LIU
;
Yuyang YANG
Author Information
1. 华北理工大学附属医院全科医学科,河北唐山 063000
- Publication Type:Journal Article
- Keywords:
Coronary artery calcification;
Homocysteine;
Prediction model
- From:
China Modern Doctor
2025;63(20):18-21
- CountryChina
- Language:Chinese
-
Abstract:
Objective To investigate the association between plasma homocysteine(Hcy)level and coronary artery calcification(CAC)and its clinical predictive efficacy.Methods A total of 172 patients who underwent coronary CT angiography North China University of Science and Technology Affiliated Hospital from April 2019 to May 2021 and CAC score(CACS)＞0 were enrolled.According to the CACS value,the subjects were divided into mild calcification group(n=136)and severe calcification group(n=36),and the clinical characteristics of two groups were compared and analyzed.Multivariate Logistic regression model was used to screen the independent influencing factors of CAC severity,and a prediction model was constructed based on the Hcy detection value.The clinical diagnostic value was evaluated by the receiver operating characteristic(ROC)curve.Results There were significant differences in Hcy,white blood cell count,triglyceride and magnesium ion levels between two groups(P＜0.05).Multivariate Logistic regression analysis showed that Hcy,white blood cell count and magnesium ion level were independent risk factors for the progression of CAC.Furthermore,a regression model based on Hcy was constructed and ROC curve was fitted to evaluate its predictive efficacy.The results suggested that the predictive model had the best performance when the critical value of Hcy was set at 27.4μmol/L:the sensitivity was 55.6％,the specificity was 97.1％,and the area under the curve was 0.765.Conclusion Hcy serves as an independent risk factor for the severity of CAC and can effectively predict the progression of CAC with high accuracy.
