Non-invasive monitoring and classification of coronary heart disease by metabolomics and lipidomics

Background: Coronary heart disease (CHD) stands as a prominent contributor to global mortality. CHD as a chronic disease usually develops over decades, and the resulting symptoms may go unnoticed until a severe blockage causes stroke or heart attack. Early screening enables timely detection of potential risk factors and signs of disease, facilitating preventive measures and lifestyle modifications. Methods: 154 suspected CHD subjects were prospectively enrolled and divided into four groups according to the results of coronary angiography. The plasma metabolites and lipids were detected by three MS-based platforms. OPLSDA was performed to discriminate the metabolic profiles between different groups. The differential metabolites were screened by statistical analysis ( p 1.5). The associations of metabolites with stenosis and clinical markers were visualized by the Spearman’s rank correlation. Results: CHD exhibited a distinct metabolic pattern, mainly involving organic acids, organoheterocyclic compounds and lipids. The perturbated metabolites possess the diagnostic potential and exhibited correlations with CHD clinical markers. Therein, specific triglyceride (TAG) species and N-epsilon,N-epsilon,N-epsilon-trimethyllysine were associated with CHD progression. We also found that TAG species have the potential to be metabolic risk factors for co-occurring CHD and diabetes. Furthermore, cluster of metabolites, primarily composed of amino acids and lipids, were significantly associated with the progression of coronary artery stenosis. Conclusions: Blood metabolites and lipids are promising as innovative tools for accurate diagnosis, progression monitoring, deciphering and management of cardiovascular diseases.