Literature DB >> 23739151

Metabolites associate with kidney function decline and incident chronic kidney disease in the general population.

Oemer-Necmi Goek1, Cornelia Prehn, Peggy Sekula, Werner Römisch-Margl, Angela Döring, Christian Gieger, Margit Heier, Wolfgang Koenig, Rui Wang-Sattler, Thomas Illig, Karsten Suhre, Jerzy Adamski, Anna Köttgen, Christa Meisinger.   

Abstract

BACKGROUND: Serum metabolites are associated cross-sectionally with kidney function in population-based studies.
METHODS: Using flow injection and liquid chromatography tandem mass spectrometry methods, we examined longitudinal associations of baseline concentrations of 140 metabolites and their 19 460 ratios with kidney function decline and chronic kidney disease (CKD) incidence over 7 years in 1104 participants of the Cooperative Health Research in the Region of Augsburg S4/F4 study.
RESULTS: Corrected for multiple testing, a significant association with annual change in the estimated glomerular filtration rate was observed for spermidine (P = 5.8 × 10(-7)) and two metabolite ratios, the phosphatidylcholine diacyl C42:5-to-phosphatidylcholine acyl-alkyl C36:0 ratio (P = 1.5 × 10(-6)) and the kynurenine-to-tryptophan ratio (P = 1.9 × 10(-6)). The kynurenine-to-tryptophan ratio was also associated with significantly higher incidence of CKD at the follow-up visit with an odds ratio of 1.36 per standard deviation increase; 95% confidence interval 1.11-1.66, P = 2.7 × 10(-3)). In separate analyses, the predictive ability of the metabolites was assessed: both the three significantly associated metabolite (ratios) as well as a panel of 35 metabolites selected from all metabolites in an unbiased fashion provided as much but not significantly more prognostic information than selected clinical predictors as judged by the area under the curve.
CONCLUSIONS: Baseline serum concentrations of spermidine and two metabolite ratios were associated with kidney function change over subsequent years in the general population. In separate analyses, baseline serum metabolites were able to predict incident CKD to a similar but not better extent than selected clinical parameters. Our longitudinal findings provide a basis for targeted studies of certain metabolic pathways, e.g. tryptophan metabolism, and their relation to kidney function decline.

Entities:  

Keywords:  GFR; incident chronic kidney disease; kidney function loss; metabolites; prediction

Mesh:

Year:  2013        PMID: 23739151     DOI: 10.1093/ndt/gft217

Source DB:  PubMed          Journal:  Nephrol Dial Transplant        ISSN: 0931-0509            Impact factor:   5.992


  48 in total

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Journal:  Pediatr Nephrol       Date:  2019-08-19       Impact factor: 3.714

2.  Tissue, urine and blood metabolite signatures of chronic kidney disease in the 5/6 nephrectomy rat model.

Authors:  Munsoor A Hanifa; Martin Skott; Raluca G Maltesen; Bodil S Rasmussen; Søren Nielsen; Jørgen Frøkiær; Troels Ring; Reinhard Wimmer
Journal:  Metabolomics       Date:  2019-08-17       Impact factor: 4.290

3.  Urine Metabolite Profiles Predictive of Human Kidney Allograft Status.

Authors:  Karsten Suhre; Joseph E Schwartz; Vijay K Sharma; Qiuying Chen; John R Lee; Thangamani Muthukumar; Darshana M Dadhania; Ruchuang Ding; David N Ikle; Nancy D Bridges; Nikki M Williams; Gabi Kastenmüller; Edward D Karoly; Robert P Mohney; Michael Abecassis; John Friedewald; Stuart J Knechtle; Yolanda T Becker; Benjamin Samstein; Abraham Shaked; Steven S Gross; Manikkam Suthanthiran
Journal:  J Am Soc Nephrol       Date:  2015-06-05       Impact factor: 10.121

4.  Integrin-mediated type II TGF-β receptor tyrosine dephosphorylation controls SMAD-dependent profibrotic signaling.

Authors:  Xiwu Chen; Hongtao Wang; Hong-Jun Liao; Wen Hu; Leslie Gewin; Glenda Mernaugh; Sheng Zhang; Zhong-Yin Zhang; Lorenzo Vega-Montoto; Roberto M Vanacore; Reinhard Fässler; Roy Zent; Ambra Pozzi
Journal:  J Clin Invest       Date:  2014-07-01       Impact factor: 14.808

5.  1,5-Anhydroglucitol predicts CKD progression in macroalbuminuric diabetic kidney disease: results from non-targeted metabolomics.

Authors:  Gesiane Tavares; Gabriela Venturini; Kallyandra Padilha; Roberto Zatz; Alexandre C Pereira; Ravi I Thadhani; Eugene P Rhee; Silvia M O Titan
Journal:  Metabolomics       Date:  2018-02-27       Impact factor: 4.290

6.  Metabolite markers of incident CKD risk.

Authors:  Eugene P Rhee; Harold I Feldman
Journal:  Clin J Am Soc Nephrol       Date:  2014-07-10       Impact factor: 8.237

7.  Associations of Plasma Amino Acid and Acylcarnitine Profiles with Incident Reduced Glomerular Filtration Rate.

Authors:  Feijie Wang; Liang Sun; Qi Sun; Liming Liang; Xianfu Gao; Rongxia Li; An Pan; Huaixing Li; Yueyi Deng; Frank B Hu; Jiarui Wu; Rong Zeng; Xu Lin
Journal:  Clin J Am Soc Nephrol       Date:  2018-03-08       Impact factor: 8.237

8.  Microbiome-metabolomics reveals gut microbiota associated with glycine-conjugated metabolites and polyamine metabolism in chronic kidney disease.

Authors:  Ya-Long Feng; Gang Cao; Dan-Qian Chen; Nosratola D Vaziri; Lin Chen; Jun Zhang; Ming Wang; Yan Guo; Ying-Yong Zhao
Journal:  Cell Mol Life Sci       Date:  2019-05-30       Impact factor: 9.261

Review 9.  An overview of renal metabolomics.

Authors:  Sahir Kalim; Eugene P Rhee
Journal:  Kidney Int       Date:  2016-09-28       Impact factor: 10.612

Review 10.  The gut microbiota and the brain-gut-kidney axis in hypertension and chronic kidney disease.

Authors:  Tao Yang; Elaine M Richards; Carl J Pepine; Mohan K Raizada
Journal:  Nat Rev Nephrol       Date:  2018-07       Impact factor: 28.314

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