The Association Between Serum Total Bile Acid Level and Long-Term Prognosis in Patients with Coronary Chronic Total Occlusion Undergoing Percutaneous Coronary Intervention

Overview

Journal Dis Markers

Publisher Wiley

Specialty Biochemistry

Date 2022 Jul 5

PMID 35783015

Authors

Xinchun Zeng

Zhijie Jian

Shanshan Li

Yu Xu

Bolin Li

Ning Ding

YiQiong Zhang

Hui Zhang

Yue Wu

Jian Yang

Zuyi Yuan

Lele Cheng

Affiliations

Soon will be listed here.

Abstract

Background And Aims: Bile acids, the key products for elimination of cholesterol, play an important role in coronary artery disease (CAD). However, few studies focused on the roles of more accessible serum total bile acids (TBA) in the prediction of adverse cardiovascular events for coronary chronic artery occlusion (CTO). The aim of this study was to explore the potential relationship between serum TBA and long-term prognosis in patients with CTO undergoing percutaneous coronary intervention (PCI).

Methods: Baseline TBA was determined in 613 patients with CTO after PCI in the present study. All patients were divided into 3 groups according to the median (3.5 mol/l) and the normal upper limit of the TBA (10 mol/l). The primary endpoint was all-cause mortality, and the secondary endpoint was major adverse cardiovascular events (MACE).

Results: Average age in this study was 65.44 ± 9.94 years old. The median of TBA was 3.5 (2.1-6.1) mol/l. Over a median follow-up of 33.5 months, compared to those with below 3.5 mol/l TBA, 3.5 ~ 10 mol/l TBA was associated with significantly reduced risk for the MACE (hazard ratio (HR): 0.59, 95% confidence interval (CI): 0.40 to 0.88; = 0.009) even after adjustment for baseline variables. However, TBA did not predict all-cause mortality and cardiovascular death. Spline analyses showed an L-shaped relationship of the serum TBA with the incidence of MACE.

Conclusions: Moderate fasting serum TBA level has a predictive value for MACE even after adjusting for lifestyle and clinical risk factors in CTO patients undergoing PCI.

Citing Articles

Machine learning-based analysis of risk factors for chronic total occlusion in an Asian population.

Shi Y, Cheng Z, Jian W, Liu Y, Liu J J Int Med Res. 2023; 51(10):3000605231202141.

PMID: 37818654 PMC: 10566279. DOI: 10.1177/03000605231202141.

Leveraging Machine Learning Techniques to Forecast Chronic Total Occlusion before Coronary Angiography.

Shi Y, Zheng Z, Liu Y, Wu Y, Wang P, Liu J J Clin Med. 2022; 11(23).

PMID: 36498568 PMC: 9739483. DOI: 10.3390/jcm11236993.

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