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J Am Soc Nephrol. 2016 Apr;27(4):1175-88. doi: 10.1681/ASN.2014111099. Epub 2015 Oct 8.

A Metabolome-Wide Association Study of Kidney Function and Disease in the General Population.

Author information

1
Division of Nephrology and Center for Medical Biometry and Medical Informatics, Medical Center-University of Freiburg, Freiburg, Germany;
2
Division of Nephrology and.
3
Department of Twin Research and Genetic Epidemiology, King's College London, London, United Kingdom;
4
Department of Twin Research and Genetic Epidemiology, King's College London, London, United Kingdom; Department of Nephrology, Hospital del Mar, Institut Mar d'Investigacions Mediques, Barcelona, Spain;
5
Division of Nephrology, Tufts Medical Center, Boston, Massachusetts;
6
Institutes of Bioinformatics and Systems Biology.
7
Research Unit of Molecular Epidemiology and.
8
Experimental Genetics, Genome Analysis Center, German Center for Diabetes Research, Neuherberg, Germany; Institute of Experimental Genetics, Technical University of Munich, Freising-Weihenstephan, Germany;
9
Institute of Functional Genomics, University of Regensburg, Regensburg, Germany;
10
Research Unit of Molecular Epidemiology and Hannover Unified Biobank and Institute for Human Genetics, Hannover Medical School, Hannover, Germany;
11
Computational Biology, and.
12
Department of Twin Research and Genetic Epidemiology, King's College London, London, United Kingdom; Academic Rheumatology, University of Nottingham, Nottingham, United Kingdom;
13
Epidemiology II, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany;
14
Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland;
15
Metabolon Inc., Durham, North Carolina; and.
16
Institutes of Bioinformatics and Systems Biology, Department of Physiology and Biophysics, Weill Cornell Medical College-Qatar, Doha, Qatar.
17
Department of Twin Research and Genetic Epidemiology, King's College London, London, United Kingdom; Institutes of Bioinformatics and Systems Biology, German Center for Diabetes Research, Neuherberg, Germany; anna.koettgen@uniklinik-freiburg.de g.kastenmueller@helmholtz-muenchen.de.
18
Division of Nephrology and anna.koettgen@uniklinik-freiburg.de g.kastenmueller@helmholtz-muenchen.de.

Abstract

Small molecules are extensively metabolized and cleared by the kidney. Changes in serum metabolite concentrations may result from impaired kidney function and can be used to estimate filtration (e.g., the established marker creatinine) or may precede and potentially contribute to CKD development. Here, we applied a nontargeted metabolomics approach using gas and liquid chromatography coupled to mass spectrometry to quantify 493 small molecules in human serum. The associations of these molecules with GFR estimated on the basis of creatinine (eGFRcr) and cystatin C levels were assessed in ≤1735 participants in the KORA F4 study, followed by replication in 1164 individuals in the TwinsUK registry. After correction for multiple testing, 54 replicated metabolites significantly associated with eGFRcr, and six of these showed pairwise correlation (r≥0.50) with established kidney function measures: C-mannosyltryptophan, pseudouridine, N-acetylalanine, erythronate, myo-inositol, and N-acetylcarnosine. Higher C-mannosyltryptophan, pseudouridine, and O-sulfo-L-tyrosine concentrations associated with incident CKD (eGFRcr <60 ml/min per 1.73 m(2)) in the KORA F4 study. In contrast with serum creatinine, C-mannosyltryptophan and pseudouridine concentrations showed little dependence on sex. Furthermore, correlation with measured GFR in 200 participants in the AASK study was 0.78 for both C-mannosyltryptophan and pseudouridine concentration, and highly significant associations of both metabolites with incident ESRD disappeared upon adjustment for measured GFR. Thus, these molecules may be alternative or complementary markers of kidney function. In conclusion, our study provides a comprehensive list of kidney function-associated metabolites and highlights potential novel filtration markers that may help to improve the estimation of GFR.

KEYWORDS:

CKD; GFR; epidemiology; metabolism; outcomes

PMID:
26449609
PMCID:
PMC4814172
DOI:
10.1681/ASN.2014111099
[Indexed for MEDLINE]
Free PMC Article

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