Chemometrics strategy coupled with high resolution mass spectrometry for analyzing and interpreting comprehensive metabolomic characterization of hyperlipemia. Issue 113 (28th November 2016)

• Record Type: Journal Article
• Authors: Zhao, Qiqi; Zhang, Aihua; Zong, Wenjing; An, Na; Zhang, Huamin; Luan, Yihan; Cao, Hongxin; Sun, Hui; Wang, Xijun
• Is Part Of: RSC advances. Volume 6:Issue 113(2016)
• Page Start: 112534
• Page End: 112543
• Publication Date: 2016-11-28
• Subjects: Chemistry -- Periodicals; 540.5
• DOI: 10.1039/c6ra24267g
• Languages: English
• ISSNs: 2046-2069

Abstract

Abstract : Hyperlipidemia (HLP) is a metabolic disorder which is characterized by a disturbance in lipid metabolism and is a primary risk factor for cardiovascular disease and atherosclerosis. Abstract : Hyperlipidemia (HLP) is a metabolic disorder which is characterized by a disturbance in lipid metabolism and is a primary risk factor for cardiovascular disease and atherosclerosis. Patients who were diagnosed with hyperlipidemia are usually with a serious dyslipidemia, which can be evaluated by current clinical markers such as triglyceride, total cholesterol, low density lipoprotein cholesterol, and high-density lipoprotein. Metabolic profiling has great potential to help the diagnosis and prognosis of hyperlipidemia patients in an earlier stage. To explore the metabolic profiling, we used ultraperformance liquid chromatography mass spectrometry, monitored the metabolites' alterations and impaired pathways in HLP patients. Of note, a total of 37 serum metabolites from HLP patients were tentatively identified. Furthermore, they were involved in 11 pathways such as linoleic acid metabolism, glycerophospholipid metabolism, terpenoid backbone biosynthesis, sphingolipid metabolism, glycosylphosphatidylinositol-anchor biosynthesis, steroid hormone biosynthesis, arachidonic acid metabolism, pentose and glucuronate interconversions, starch and sucrose metabolism, glycerolipid metabolism and purine metabolism. These results provide potential biomarkers for early risk assessment of HLP and offer further insights into the complex metabolic pathway changes in HLP. They also suggest the distinction of utilizing UPLC-Q-TOF-MS for exploring metabolic characterization of HLP patients.