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  • 标题:Top-down lipidomics of low density lipoprotein reveal altered lipid profiles in advanced chronic kidney disease
  • 本地全文:下载
  • 作者:Ana Reis ; Alisa Rudnitskaya ; Pajaree Chariyavilaskul
  • 期刊名称:JLR Papers In Press
  • 印刷版ISSN:0022-2275
  • 电子版ISSN:1539-7262
  • 出版年度:2015
  • 卷号:56
  • 期号:2
  • 页码:413-422
  • DOI:10.1194/jlr.M055624
  • 语种:English
  • 出版社:American Society for Biochemistry and Molecular Biology
  • 摘要:This study compared the molecular lipidomic profile of LDL in patients with nondiabetic advanced renal disease and no evidence of CVD to that of age-matched controls, with the hypothesis that it would reveal proatherogenic lipid alterations. LDL was isolated from 10 normocholesterolemic patients with stage 4/5 renal disease and 10 controls, and lipids were analyzed by accurate mass LC/MS. Top-down lipidomics analysis and manual examination of the data identified 352 lipid species, and automated comparative analysis demonstrated alterations in lipid profile in disease. The total lipid and cholesterol content was unchanged, but levels of triacylglycerides and N -acyltaurines were significantly increased, while phosphatidylcholines, plasmenyl ethanolamines, sulfatides, ceramides, and cholesterol sulfate were significantly decreased in chronic kidney disease (CKD) patients. Chemometric analysis of individual lipid species showed very good discrimination of control and disease sample despite the small cohorts and identified individual unsaturated phospholipids and triglycerides mainly responsible for the discrimination. These findings illustrate the point that although the clinical biochemistry parameters may not appear abnormal, there may be important underlying lipidomic changes that contribute to disease pathology. The lipidomic profile of CKD LDL offers potential for new biomarkers and novel insights into lipid metabolism and cardiovascular risk in this disease.
  • 关键词:c holesterol ; dyslipidemias ; inflammation ; phospholipids ; mass spectrometry ; cholesterol sulfate ; N -acyltaurine ; partial least squares discriminant analysis
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