Metabolomic profiling to improve glomerular filtration rate estimation: a proof-of-concept study

Estimation of glomerular filtration rate (GFR) using estimated glomerular filtration rate creatinine (eGFRcr) is central to clinical practice but has limitations. We tested the hypothesis that serum metabolomic profiling can identify novel markers that in combination can provide more accurate GFR es...

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Published in:Nephrology, dialysis, transplantation dialysis, transplantation, 2019-05, Vol.34 (5), p.825-833
Main Authors: Coresh, Josef, Inker, Lesley A, Sang, Yingying, Chen, Jingsha, Shafi, Tariq, Post, Wendy S, Shlipak, Michael G, Ford, Lisa, Goodman, Kelli, Perichon, Regis, Greene, Tom, Levey, Andrew S
Format: Article
Language:English
Subjects:
Adult
Aged
Aged, 80 and over
Biomarkers - blood
Chromatography, Liquid
Creatinine - blood
Cross-Sectional Studies
Cystatin C - blood
Female
Follow-Up Studies
Glomerular Filtration Rate - physiology
Humans
Male
Metabolomics - methods
Middle Aged
ORIGINAL ARTICLES
Proof of Concept Study
Renal Insufficiency, Chronic - blood
Renal Insufficiency, Chronic - physiopathology
Time Factors
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Summary:Estimation of glomerular filtration rate (GFR) using estimated glomerular filtration rate creatinine (eGFRcr) is central to clinical practice but has limitations. We tested the hypothesis that serum metabolomic profiling can identify novel markers that in combination can provide more accurate GFR estimates. We performed a cross-sectional study of 200 African American Study of Kidney Disease and Hypertension (AASK) and 265 Multi-Ethnic Study of Atherosclerosis (MESA) participants with measured GFR (mGFR). Untargeted gas chromatography/dual mass spectrometry- and liquid chromatography/dual mass spectrometry-based quantification was followed by the development of targeted assays for 15 metabolites. On the log scale, GFR was estimated from single- and multiple-metabolite panels and compared with eGFR using the Chronic Kidney Disease Epidemiology equations with creatinine and/or cystatin C using established metrics, including the proportion of errors >30% of mGFR (1-P30), before and after bias correction. Of untargeted metabolites in the AASK and MESA, 283 of 780 (36%) and 387 of 1447 (27%), respectively, were significantly correlated (P ≤ 0.001) with mGFR. A targeted metabolite panel eGFR developed in the AASK and validated in the MESA was more accurate (1-P30 3.7 and 1.9%, respectively) than eGFRcr [11.2 and 18.5%, respectively (P 
ISSN:0931-0509
1460-2385
DOI:10.1093/ndt/gfy094