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Untargeted Metabolomics Reveals N, N, N-trimethyl-L-alanyl-L-proline Betaine (TMAP) As a Novel Biomarker of Kidney Function

Overview
Journal Sci Rep
Specialty Science
Date 2019 May 4
PMID 31048706
Citations 18
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Abstract

The diagnosis and prognosis of chronic kidney disease (CKD) currently relies on very few circulating small molecules, which can vary by factors unrelated to kidney function. In end-stage renal disease (ESRD), these same small molecules are used to determine dialysis dose and dialytic clearance. Therefore, we aimed to identify novel plasma biomarkers to estimate kidney function in CKD and dialytic clearance in ESRD. Untargeted metabolomics was performed on plasma samples from patients with a single kidney, non-dialysis CKD, ESRD and healthy controls. For ESRD patients, pre- and post-dialysis plasma samples were obtained from several dialysis modalities. Metabolomics analysis revealed over 400 significantly different features in non-dialysis CKD and ESRD plasma compared to controls while less than 35 features were significantly altered in patients with a single kidney. N,N,N-trimethyl-L-alanyl-L-proline betaine (TMAP, AUROC = 0.815) and pyrocatechol sulfate (AUROC = 0.888) outperformed creatinine (AUROC = 0.745) in accurately identifying patients with a single kidney. Several metabolites accurately predicted ESRD; however, when comparing pre-and post-hemodialysis, TMAP was the most robust biomarker of dialytic clearance for all modalities (AUROC = 0.993). This study describes TMAP as a novel potential biomarker of kidney function and dialytic clearance across several hemodialysis modalities.

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References
1.
Zhang Q, Ford L, Evans A, Toal D . Structure elucidation of metabolite x17299 by interpretation of mass spectrometric data. Metabolomics. 2017; 13(8):92. PMC: 5486616. DOI: 10.1007/s11306-017-1231-x. View

2.
Hocher B, Adamski J . Metabolomics for clinical use and research in chronic kidney disease. Nat Rev Nephrol. 2017; 13(5):269-284. DOI: 10.1038/nrneph.2017.30. View

3.
Smith C, Want E, OMaille G, Abagyan R, Siuzdak G . XCMS: processing mass spectrometry data for metabolite profiling using nonlinear peak alignment, matching, and identification. Anal Chem. 2006; 78(3):779-87. DOI: 10.1021/ac051437y. View

4.
Xia J, Wishart D . Using MetaboAnalyst 3.0 for Comprehensive Metabolomics Data Analysis. Curr Protoc Bioinformatics. 2016; 55:14.10.1-14.10.91. DOI: 10.1002/cpbi.11. View

5.
Meyer T, Hostetter T . Uremia. N Engl J Med. 2007; 357(13):1316-25. DOI: 10.1056/NEJMra071313. View