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Plasma metabolites has proved an indispensable regulators of physiological and pathological processes in coronary artery disease (CAD). However, the complex changes in metabolites that occur in CAD patients with different complications are incompletely understood. Diabetes mellitus (DM), one of the most common complications and independent risk factor in CAD, usually aggravate the deterioration of cardiac function. In this study, we performed ultra performance liquid chromatography and quadrupole time-of-flight mass spectrometry (UPLC-Q-TOF MS) based metabolic profiling in plasma of unstable angina (UA) complicated with DM patients, to detect potential metabolic biomarkers and pathways.
We performed metabolomics profiling to identify alterations in patients with UA and complicated with DM. From 2014 to 2015 in the Cardiovascular Department of Affiliated Teaching Hospital of Beijing University of Chinese Medicine,20 patients with isolated UA were served as A group,20 UA complicated with type 2 DM patients were served as B group,20 healthy cases were served as C group. Global metabolomics profiling was applied to plasma metabolites from patients and age-, sex-, and body mass index-matched subjects using UPLC-Q-TOF MS and multivariate statistical analysis. The construction, interaction and pathway analysis of potential biomarkers were analyzed by Met PA (Metabolomics Pathway Analysis) and database sources, including the KEGG, the Human Metabolome database, and METLIN, were used to identify the related metabolic pathways.
A multivariate analysis showed a clear separation between the patients with UA, complicated with DM patients and normal controls. Based on the comprehensive plasma metabolic candidates, we identified twenty-nine potential metabolic biomarkers, with Lysopho- sphatidylcholine (lysoPC) and lysophosphatidylethanolamine (lysoPE) species containing unsaturated fatty acids and free fatty acids,creatinine, palmitylacetate, L-Tryptophan showing the best classification performance. The underlying regulations of metabolic pathways are analyzed according to the identified metabolites, and seven metabolic pathways are identified using MetPA (impact score>0.15). Metabolic pathways including energy metabolism, antioxidant defense systems and phospholipid metabolism were found to be disturbed. Additionally, we found strong positive correlation between tryptophan metabolism and compound related with insulin resistance in UA complicated with DM patients, which indicated further understanding of pathological mechanisms involved. Further study of these metabolites may also provide some references to identification of metabolic biomarkers for diagnosis and risk prediction of UA complicated with DM.
It was concluded that the UPLC-Q-TOF MS based metabolomics approach combined with pattern recognition methods demonstrated good performance to identify metabolic plasma markers and provided new insights into biological mechanisms underlying UA complicated with DM.