Lipidomics Facilitates the Discovery of Diagnostic Biomarkers in Patients with Chronic Total Occlusion during the Perioperative Period

Chronic total occlusion (CTO) is a subtype of cardiovascular disease associated with high mortality and an increased risk of ventricular arrhythmia. This study aimed to investigate lipidomic changes in CTO patients undergoing percutaneous coronary intervention (PCI) using a tandem-lipidomic strategy...

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Main Author: Zhenxin Wang (730316) (author)
Other Authors: Xin Zhong (464533) (author), Kaidi Wang (205522) (author), Lei Zhang (38117) (author), Hua Li (46469) (author), Yingfei Peng (21482617) (author), Fangjun Chen (6296003) (author), Wei Guo (86150) (author), Pengyuan Yang (135243) (author), Quanqing Zhang (1699543) (author), Huali Shen (624840) (author), Ling Lin (57361) (author)
Published: 2025
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Summary:Chronic total occlusion (CTO) is a subtype of cardiovascular disease associated with high mortality and an increased risk of ventricular arrhythmia. This study aimed to investigate lipidomic changes in CTO patients undergoing percutaneous coronary intervention (PCI) using a tandem-lipidomic strategy. We first applied a global lipidomic approach to identify the serum lipidomes of CTO-PCI patients during the perioperative period, successfully separating and identifying over 1500 lipids. Based on these results, a multiple reaction monitoring (MRM) quantification method was developed and employed for targeted lipidomic analysis. Using a high-throughput MRM tandem liquid chromatography–mass spectrometry approach, 613 lipids were successfully quantified in CTO-PCI patients and control donors. The lipid profile of PAs has surfaced as a potential biomarker that can help tell CTO patients apart from individuals suspected of having the conditional biomarker for distinguishing CTO patients from those suspected of having the condition. Notably, patients with different prognostic outcomes exhibited significantly distinct serum lipidomes in both pre- and post-CTO-PCI samples. This finding suggests that lipidomic data hold significant potential for both monitoring postoperative prognosis and predicting surgical outcomes.