Coal Worker's Pneumoconiosis-Targeted Lipidomics Reveals Aberrant Phospholipid Metabolism for Early-Stage Diagnosis

煤工尘肺靶向脂质组学揭示异常磷脂代谢,可用于早期诊断

阅读:1

Abstract

INTRODUCTION: Pneumoconiosis is the most prevalent occupational disease in China, with coal worker pneumoconiosis (CWP) demonstrating the highest incidence. Studies have indicated that phospholipids may be associated with CWP. METHODS: In this study, serum was obtained from 62 patients with pneumoconiosis, 105 coal dust-exposed workers, and 50 healthy individuals and analyzed via targeted lipidomics using ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS). After initially identifying phospholipids with significant differences through univariate and multivariate statistical analyses, receiver operating characteristic (ROC) analysis was performed. The differential phospholipids identified in patient samples were then integrated to assess their diagnostic potential for CWP using a support vector machine (SVM). RESULTS: Compared with healthy subjects, the levels of Lyso-PS (18:0) were decreased, while PC (16:0), PC (18:0), PC (16:0/18:1), PI (16:0/18:1), PS (18:1), PG (16:0), and PG (18:0/18:1) were significantly increased in the pneumoconiosis group, with an area under the curve (AUC)>0.7. Moreover, compared with the dust-exposed group, Lyso-PC (16:0), PC (16:0), PC (16:0/18:1), PI (16:0/18:1), and PG (16:0) were significantly elevated in the pneumoconiosis group, with an AUC>0.7. The diagnostic model, including PC (16:0), PC (16:0/18:1), PI (16:0/18:1), and PG (16:0), demonstrated excellent performance with an AUC of 0.956. DISCUSSION: The serum phospholipid profiles of patients with pneumoconiosis differed significantly from those of controls, including differences in PC, Lyso-PC, PI, PS, Lyso-PS, and PG. Among these, a diagnostic model incorporating PC (16:0), PC (16:0/18:1), PI (16:0/18:1), and PG (16:0) demonstrated superior screening efficiency.

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。