New sequential algorithm using Mac-2 binding protein glycosylation isomer to detect advanced carotid artery atherosclerosis

利用Mac-2结合蛋白糖基化异构体检测晚期颈动脉粥样硬化的新型序列算法

阅读:2

Abstract

BACKGROUND: Although carotid artery sonography is widely performed, most guidelines do not recommend this procedure in the general population. Appropriate indications and effective algorithms are needed to detect advanced carotid artery atherosclerosis in a community setting. METHODS: This study was designed as cross-sectional study. Adult subjects (n=228) who underwent a health check-up at our healthcare centre were included in the final analysis. Mac-2 binding protein glycosylation isomer (M2BPGi) quantification was based on a lectin antibody sandwich immunoassay. Subclinical atherosclerosis was diagnosed by carotid ultrasonography. RESULTS: The prevalence of subclinical atherosclerosis and advanced atherosclerosis was 37.2% (85/228) and 11.8% (27/228), respectively, in a community-based setting. Serum M2BPGi level was significantly higher in subjects with calcified plaque (0.6317) and luminal stenosis (0.6373) than in control groups (0.4913, all P<0.05). Pearson correlation analysis between M2BPGi and atherosclerotic cardiovascular disease (ASCVD) risk index (R=0.410, P<0.001) showed a positive relationship. The AUROC of serum M2BPGi for identifying calcified plaque or luminal stenosis was 0.679. The sequential algorithm using ASCVD and M2BPGi showed good negative predictive value (NPV) (93.6%) and reasonable positive predictive value (PPV) (53.8%) for identifying calcified plaque or luminal stenosis. When the sequential algorithm was used as an indicator for carotid ultrasonography, 35.0% (14/40) of subjects with intermediate-risk by ASCVD (≥7.5%) could avoid unnecessary carotid ultrasonography. CONCLUSIONS: The sequential algorithm using ASCVD (≥7.5) and M2BPGi (≥0.525) provided reasonable indication for carotid artery sonography in a community-based setting.

特别声明

1、本页面内容包含部分的内容是基于公开信息的合理引用;引用内容仅为补充信息,不代表本站立场。

2、若认为本页面引用内容涉及侵权,请及时与本站联系,我们将第一时间处理。

3、其他媒体/个人如需使用本页面原创内容,需注明“来源:[生知库]”并获得授权;使用引用内容的,需自行联系原作者获得许可。

4、投稿及合作请联系:info@biocloudy.com。