Lipid metabolomics identifies novel biomarkers for diagnosis and monitoring of coronary slow flow

Yonglong Ye; Yunxian Chen; Zhidong Wan; Huiying Pan; Zhijun Hou; Jun Liu

doi:10.1016/j.cca.2025.120434

Lipid metabolomics identifies novel biomarkers for diagnosis and monitoring of coronary slow flow

Clin Chim Acta. 2025 Jun 16:577:120434. doi: 10.1016/j.cca.2025.120434. Online ahead of print.

Authors

Yonglong Ye¹, Yunxian Chen², Zhidong Wan¹, Huiying Pan¹, Zhijun Hou³, Jun Liu⁴

Affiliations

¹ Department of Laboratory Medicine, Dongguan Hospital of Guangzhou University of Chinese Medicine, Dongguan 523005, China.
² Department of Cardiology, Yue Bei People's Hospital, Shantou University Medical College, Shaoguan 512000, China.
³ Department of Cardiology, Dongguan Hospital of Guangzhou University of Chinese Medicine, Dongguan 523005, China.
⁴ Department of Laboratory Medicine, Dongguan Hospital of Guangzhou University of Chinese Medicine, Dongguan 523005, China. Electronic address: liuyu8566@126.com.

PMID: 40532807
DOI: 10.1016/j.cca.2025.120434

Abstract

Coronary Slow Flow (CSF) is a significant cardiovascular disorder characterized by high incidence rates and frequently associated with adverse clinical outcomes. Early diagnosis and effective monitoring are essential for enhancing patient prognosis. Consequently, this study seeks to explore the metabolic distinctions among patients with CSF, individuals with coronary artery disease (CAD), and those with normal coronary arteries (NCA) through metabolomic analysis, with the aim of identifying potential biomarkers that could offer novel insights into the early diagnosis and treatment of CSF. This study utilized ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) to analyze 1,430 lipid metabolites. Comparative metabolomic profiling among the CSF, NCA, and CAD groups revealed significant metabolic disparities, with 66 differentially expressed metabolites identified in CSF patients. Enrichment analysis indicated that these metabolites were predominantly involved in critical metabolic pathways, including thermogenesis, glycerophospholipid metabolism, glycerolipid metabolism, and fat digestion and absorption. Subsequent analyses employing least absolute shrinkage and selection operator (LASSO) regression and random forest algorithms identified four critical biomarkers: DG(O-19:0_16:0), MG(16:0), DG(16:0_18:0), and Carnitine C6-2OH. These biomarkers demonstrated robust efficacy in differentiating between patients with CSF and those with CAD, with area under the curve values of 0.809, 0.924, 0.859, and 0.929, respectively. Furthermore, patients with CSF undergoing enhanced external counterpulsation exhibited significant symptomatic improvement, which was accompanied by a marked reduction in the expression levels of these metabolites. In conclusion, this metabolomic investigation elucidates the metabolic profile of CSF and identifies four pivotal metabolic biomarkers with potential diagnostic and prognostic utility. These results not only introduce novel biomarkers for the early detection of CSF but also provide new insights for personalized treatment strategies and therapeutic monitoring.

Keywords: Biomarkers; Coronary artery disease; Coronary slow flow; Metabolomic; UPLC-MS/MS.