Dataset Information

Identification of biomarkers for development of end-stage kidney disease in chronic kidney disease by metabolomic profiling.

ABSTRACT: A critical issue in the management of chronic kidney disease (CKD) is to prevent patients from the progression to end-stage kidney disease (ESKD), however, there is only limited number of biomarkers for the discrimination of the high-risk CKD patients. We aimed to identify the metabolites which possess the ability to predict the earlier kidney deterioration. We performed capillary electrophoresis and liquid chromatography mass spectrometry (CE-MS)-based metabolic profiling in a prospective cohort, which consisted of referred 112 CKD patients with median follow-up period of 4.4 years. The association between the levels of candidate metabolites and the outcomes (progression to ESKD alone or in combination with death before ESKD) were assessed by multivariate Cox proportional hazard models after adjusting for the baseline covariates. A total of 218 metabolites were detected in the plasma of CKD patients. We identified 16 metabolites which have predictive values for the composite outcome: The risk for composite outcome was elevated from 2.0- to 8.0-fold in those with higher levels of 16 plasma metabolites. Our results suggest that the measurement of these metabolites may facilitate CKD management by predicting the risk of progression to ESKD.

SUBMITTER: Kimura T

PROVIDER: S-EPMC4870629 | biostudies-literature | 2016 May

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Identification of biomarkers for development of end-stage kidney disease in chronic kidney disease by metabolomic profiling.

Kimura Tomonori T Yasuda Keiko K Yamamoto Ryohei R Soga Tomoyoshi T Rakugi Hiromi H Hayashi Terumasa T Isaka Yoshitaka Y

Scientific reports 20160518

A critical issue in the management of chronic kidney disease (CKD) is to prevent patients from the progression to end-stage kidney disease (ESKD), however, there is only limited number of biomarkers for the discrimination of the high-risk CKD patients. We aimed to identify the metabolites which possess the ability to predict the earlier kidney deterioration. We performed capillary electrophoresis and liquid chromatography mass spectrometry (CE-MS)-based metabolic profiling in a prospective cohor ...[more]

PMID: 27188985

Similar Datasets

Project description:Cardiovascular disease is the major cause of morbidity and death in patients with chronic kidney disease (CKD) and end-stage renal disease (ESRD). Patients with CKD and ESRD are at high risk for myocardial dysfunction, ischemia and heart failure. The mechanisms linking impaired renal function and increased risk for cardiovascular diseases, however, remain elusive. In addition, conventional therapeutics proven effective in reducing cardiovascular events in general population fail to provide similar benefits in uremic patients. There is a clear need to identify novel mediators of cardiovascular complications in uremic patients to provide insights into the pathogenesis, to tailor clinical care based on cardiovascular risks, and to develop new therapeutic strategies. It has become increasingly clear that the transcription of the eukaryotic genome is far more pervasive and complex than previously appreciated. While the expression of messenger RNAs (mRNAs) and microRNAs (miRNAs) account for only ~1% of all transcribed species, up to 90% of the mammalian genome is transcribed as long non-coding RNAs (lncRNAs), a heterogeneous group of non-coding transcripts longer than 200 nucleotides. LncRNAs have been shown to be functional and involved in specific physiological and pathological processes through epigenetic, transcriptional and post-transcriptional mechanisms. While the roles of lncRNAs in human diseases including cancer and neurodegenerative disorders are beginning to emerge, it remains unclear how lncRNA regulation contributes to cardiovascular complications in patients with renal dysfunction. In this proposal, we seek to apply next-generation sequencing technology to investigate circulating (plasma) lncRNA expression in control subjects and in patients with CKD and ESRD. We will test the hypothesis that circulating lncRNA expression signature can reflect the underlying kidney disease states in patients with CKD and ESRD. In addition, we will determine if circulating lncRNA expression signature could be a sensitive and specific biomarker to predict adverse cardiovascular events in patients with ESRD.

Dataset Information

Identification of biomarkers for development of end-stage kidney disease in chronic kidney disease by metabolomic profiling.

Publications

Identification of biomarkers for development of end-stage kidney disease in chronic kidney disease by metabolomic profiling.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets