{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,15]],"date-time":"2026-09-15T21:19:11Z","timestamp":1789507151301,"version":"build-2803163510"},"reference-count":38,"publisher":"Oxford University Press (OUP)","issue":"3","license":[{"start":{"date-parts":[[2019,3,1]],"date-time":"2019-03-01T00:00:00Z","timestamp":1551398400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"funder":[{"name":"J. Coresh","award":["#5007386-SERV"],"award-info":[{"award-number":["#5007386-SERV"]}]},{"DOI":"10.13039\/100001259","name":"National Kidney Foundation","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100001259","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003653","name":"Korea National Institute of Health","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100003653","id-type":"DOI","asserted-by":"publisher"}]},{"name":"L.A. Inker","award":["#5007386-SERV"],"award-info":[{"award-number":["#5007386-SERV"]}]},{"name":"NIA Intramural Research Program","award":["N01-AG-1\u20132100"],"award-info":[{"award-number":["N01-AG-1\u20132100"]}]},{"name":"NIA Intramural Research Program","award":["HHSN27120120022C"],"award-info":[{"award-number":["HHSN27120120022C"]}]},{"name":"NIA Intramural Research Program","award":["R01DK097020"],"award-info":[{"award-number":["R01DK097020"]}]},{"name":"A.S. Levey","award":["#5007386-SERV"],"award-info":[{"award-number":["#5007386-SERV"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019,3,1]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>BACKGROUND<\/jats:title><jats:p>Clinical practice guidelines recommend estimation of glomerular filtration rate (eGFR) using validated equations based on serum creatinine (eGFRcr), cystatin C (eGFRcys), or both (eGFRcr-cys). However, when compared with the measured GFR (mGFR), only eGFRcr-cys meets recommended performance standards. Our goal was to develop a more accurate eGFR method using a panel of metabolites without creatinine, cystatin C, or demographic variables.<\/jats:p><\/jats:sec><jats:sec><jats:title>METHODS<\/jats:title><jats:p>An ultra-performance liquid chromatography\u2013tandem mass spectrometry assay for acetylthreonine, phenylacetylglutamine, pseudouridine, and tryptophan was developed, and a 20-day, multiinstrument analytical validation was conducted. The assay was tested in 2424 participants with mGFR data from 4 independent research studies. A new GFR equation (eGFRmet) was developed in a random subset (n = 1615) and evaluated in the remaining participants (n = 809). Performance was assessed as the frequency of large errors [estimates that differed from mGFR by at least 30% (1 \u2212 P30); goal &amp;lt;10%].<\/jats:p><\/jats:sec><jats:sec><jats:title>RESULTS<\/jats:title><jats:p>The assay had a mean imprecision (\u226410% intraassay, \u22646.9% interassay), linearity over the quantitative range (r2 &amp;gt; 0.98), and analyte recovery (98.5%\u2013113%). There was no carryover, no interferences observed, and analyte stability was established. In addition, 1 \u2212 P30 in the validation set for eGFRmet (10.0%) was more accurate than eGFRcr (13.1%) and eGFRcys (12.0%) but not eGFRcr-cys (8.7%). Combining metabolites, creatinine, cystatin C, and demographics led to the most accurate equation (7.0%). Neither equation had substantial variation among population subgroups.<\/jats:p><\/jats:sec><jats:sec><jats:title>CONCLUSIONS<\/jats:title><jats:p>The new eGFRmet equation could serve as a confirmatory test for GFR estimation.<\/jats:p><\/jats:sec>","DOI":"10.1373\/clinchem.2018.288092","type":"journal-article","created":{"date-parts":[[2019,1,15]],"date-time":"2019-01-15T21:45:14Z","timestamp":1547588714000},"page":"406-418","source":"Crossref","is-referenced-by-count":23,"title":["Validation of a Metabolite Panel for a More Accurate Estimation of Glomerular Filtration Rate Using Quantitative LC-MS\/MS"],"prefix":"10.1093","volume":"65","author":[{"given":"Tiffany A","family":"Freed","sequence":"first","affiliation":[{"name":"Metabolon, Inc., Morrisville, NC"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Josef","family":"Coresh","sequence":"first","affiliation":[{"name":"Departments of Epidemiology, Medicine and Biostatistics, Johns Hopkins University, Bloomberg 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NC"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jacob E","family":"Wulff","sequence":"first","affiliation":[{"name":"Metabolon, Inc., Morrisville, NC"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gudn\u00fd","family":"Eir\u00edksd\u00f3ttir","sequence":"first","affiliation":[{"name":"Icelandic Heart Association, Kopavogur, Iceland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Vilmundur","family":"Gudnason","sequence":"first","affiliation":[{"name":"Icelandic Heart Association, Kopavogur, Iceland"},{"name":"Faculty of Medicine, University of Iceland, Reykjavik, Iceland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Vicente E","family":"Torres","sequence":"first","affiliation":[{"name":"Department of Nephrology and Hypertension, Mayo Clinic, Rochester, MN"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lisa A","family":"Ford","sequence":"first","affiliation":[{"name":"Metabolon, Inc., 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