Liu Yang1, Zhu Li1, Yanqi Song1, Yijia Liu1, Huan Zhao1, Yuechen Liu1, Tianpu Zhang1, Yu Yuan1, Xuemeng Cai1, Shuo Wang1, Pengwei Wang1, Shan Gao1, Lin Li2, Yubo Li3, Chunquan Yu4. 1. Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, West Area, Tuanbo New Town, Jinghai District, Tianjin 301617, China. 2. Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, West Area, Tuanbo New Town, Jinghai District, Tianjin 301617, China. Electronic address: llbianji@163.com. 3. Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, West Area, Tuanbo New Town, Jinghai District, Tianjin 301617, China. Electronic address: yaowufenxi001@sina.com. 4. Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, West Area, Tuanbo New Town, Jinghai District, Tianjin 301617, China. Electronic address: ycq-4@163.com.
Abstract
BACKGROUND: As a recognized risk factor for cardiovascular disease (CVD), hyperlipidemia (HLP) has developed into a high incidence disease that seriously threatens human health. Finding a new target for effective treatment of HLP will be a powerful way to reduce the incidence of CVD. The purpose of this study was to find potential biomarkers in urine of HLP patients and analyze their metabolic pathways to study the pathogenesis of HLP. METHODS: An UPLC-Q-TOF/MS technology was used to detect the metabolites in urine of 60 HLP patients and 60 normal controls. Based on PLS-DA pattern recognition, potential biomarkers related to HLP were screened out. RESULTS: 22 potential biomarkers related to HLP were identified, which involved amino acid metabolism, fatty acid metabolism, nucleotide metabolism, steroid hormone metabolism and intestinal flora metabolism, and their possible pathogenesis was found to be related to inflammatory reaction and oxidative stress. CONCLUSION: The non-targeted metabolomic method based on UPLC-Q-TOF/MS technology can effectively identify potential biomarkers in the urine of HLP patients and explore the possible pathogenesis. Our research will lay a foundation for finding new targets for the treatment of HLP and provide a basis for clinical research on the treatment of HLP.
BACKGROUND: As a recognized risk factor for cardiovascular disease (CVD), hyperlipidemia (HLP) has developed into a high incidence disease that seriously threatens human health. Finding a new target for effective treatment of HLP will be a powerful way to reduce the incidence of CVD. The purpose of this study was to find potential biomarkers in urine of HLPpatients and analyze their metabolic pathways to study the pathogenesis of HLP. METHODS: An UPLC-Q-TOF/MS technology was used to detect the metabolites in urine of 60 HLPpatients and 60 normal controls. Based on PLS-DA pattern recognition, potential biomarkers related to HLP were screened out. RESULTS: 22 potential biomarkers related to HLP were identified, which involved amino acid metabolism, fatty acid metabolism, nucleotide metabolism, steroid hormone metabolism and intestinal flora metabolism, and their possible pathogenesis was found to be related to inflammatory reaction and oxidative stress. CONCLUSION: The non-targeted metabolomic method based on UPLC-Q-TOF/MS technology can effectively identify potential biomarkers in the urine of HLPpatients and explore the possible pathogenesis. Our research will lay a foundation for finding new targets for the treatment of HLP and provide a basis for clinical research on the treatment of HLP.
Authors: Elena V Feofanova; Han Chen; Yulin Dai; Peilin Jia; Megan L Grove; Alanna C Morrison; Qibin Qi; Martha Daviglus; Jianwen Cai; Kari E North; Cathy C Laurie; Robert C Kaplan; Eric Boerwinkle; Bing Yu Journal: Am J Hum Genet Date: 2020-10-07 Impact factor: 11.025
Authors: Paula J Martinez; Marta Agudiez; Dolores Molero; Marta Martin-Lorenzo; Montserrat Baldan-Martin; Aranzazu Santiago-Hernandez; Juan Manuel García-Segura; Felipe Madruga; Martha Cabrera; Eva Calvo; Gema Ruiz-Hurtado; Maria G Barderas; Fernando Vivanco; Luis M Ruilope; Gloria Alvarez-Llamas Journal: J Mol Med (Berl) Date: 2020-09-11 Impact factor: 4.599