Untargeted metabolomics reveals biomarkers for the diagnosis of coronary artery plaques as observed by coronary cardiac computed tomography.

Atherosclerosis is a major cause of morbidity and mortality worldwide; in Israel, ischemic heart disease is the second leading cause of death for both genders aged 45 and above. Atherosclerosis involves stiffening of the arteries due to the accumulation of lipids and oxidized lipids on the blood vessel walls, triggering the development of artery plaque. Coronary artery disease (CAD) is the most common manifestation of atherosclerosis. The prevalence of CAD in the general population remains high, despite efforts to improve the identification of risk factors and preventive treatments. The discovery of new biomarkers is vital to improving the diagnosis of CAD and its risk factors. We aimed to identify novel biomarkers that could provide an early diagnosis of coronary artery atherosclerotic plaques, their type, and the percentage of stenosis. We used an untargeted metabolomics approach to identify potential biomarkers that could enable highly sensitive and specific CAD detection. The study consisted of 109 patients who underwent cardiac computed tomography angiography at the Cardiology Department of Ziv Medical Center. Fifty-four patients were diagnosed with coronary atherosclerotic plaques (CAD group), and 55 without plaques used control. Untargeted metabolomics using LC-MS/MS revealed 2560 metabolites in the patients' serum: 106 showed statistically significant upregulation in the serum of the CAD group compared with the healthy control group (p < 0.05). These metabolites belonged to the following chemical families: acyl-carnitines, cyclodipeptides, lysophosphatidylcholine, and primary bile acids. In contrast, 98 metabolites displayed statistically significant downregulation in the serum of the CAD group compared with the control group, belonging to the following chemical families: GABA amino acids and derivatives (inhibitory neurotransmitters), lipids, and secondary bile acids. Our comprehensive untargeted serum metabolomic analysis revealed biomarkers that can be used for the diagnosis of patients with CAD. Further cohort studies with a larger number of participants are needed to validate the detected biomarkers.