{"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":1789507151180,"version":"build-2803163510"},"reference-count":47,"publisher":"Ovid Technologies (Wolters Kluwer Health)","issue":"8","funder":[{"DOI":"10.13039\/100000062","name":"NIDDK","doi-asserted-by":"publisher","award":["U01DK106982"],"award-info":[{"award-number":["U01DK106982"]}],"id":[{"id":"10.13039\/100000062","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000062","name":"NIDDK","doi-asserted-by":"publisher","award":["U01DK085689"],"award-info":[{"award-number":["U01DK085689"]}],"id":[{"id":"10.13039\/100000062","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000062","name":"NIDDK","doi-asserted-by":"publisher","award":["U01DK103225"],"award-info":[{"award-number":["U01DK103225"]}],"id":[{"id":"10.13039\/100000062","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000062","name":"NIDDK","doi-asserted-by":"publisher","award":["K23DK093556"],"award-info":[{"award-number":["K23DK093556"]}],"id":[{"id":"10.13039\/100000062","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["lww.com","ovid.com"],"crossmark-restriction":true},"short-container-title":["CJASN"],"published-print":{"date-parts":[[2021,8]]},"abstract":"<jats:sec>\n                    <jats:title>Background and objectives<\/jats:title>\n                    <jats:p>Metabolomics facilitates the discovery of biomarkers and potential therapeutic targets for CKD progression.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Design, setting, participants, &amp; measurements<\/jats:title>\n                    <jats:p>We evaluated an untargeted metabolomics quantification of stored plasma samples from 645 Chronic Kidney Disease in Children (CKiD) participants. Metabolites were standardized and logarithmically transformed. Cox proportional hazards regression examined the association between 825 nondrug metabolites and progression to the composite outcome of KRT or 50% reduction of eGFR, adjusting for age, sex, race, body mass index, hypertension, glomerular versus nonglomerular diagnosis, proteinuria, and baseline eGFR. Stratified analyses were performed within subgroups of glomerular\/nonglomerular diagnosis and baseline eGFR.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Baseline characteristics were 391 (61%) male; median age 12 years; median eGFR 54 ml\/min per 1.73 m\n                      <jats:sup>2<\/jats:sup>\n                      ; 448 (69%) nonglomerular diagnosis. Over a median follow-up of 4.8 years, 209 (32%) participants developed the composite outcome. Unique association signals were identified in subgroups of baseline eGFR. Among participants with baseline eGFR \u226560 ml\/min per 1.73 m\n                      <jats:sup>2<\/jats:sup>\n                      , two-fold higher levels of seven metabolites were significantly associated with higher hazards of KRT\/halving of eGFR events: three involved in purine and pyrimidine metabolism (N6-carbamoylthreonyladenosine, hazard ratio, 16; 95% confidence interval, 4 to 60; 5,6-dihydrouridine, hazard ratio, 17; 95% confidence interval, 5 to 55; pseudouridine, hazard ratio, 39; 95% confidence interval, 8 to 200); two amino acids, C-glycosyltryptophan, hazard ratio, 24; 95% confidence interval 6 to 95 and lanthionine, hazard ratio, 3; 95% confidence interval, 2 to 5; the tricarboxylic acid cycle intermediate 2-methylcitrate\/homocitrate, hazard ratio, 4; 95% confidence interval, 2 to 7; and gulonate, hazard ratio, 10; 95% confidence interval, 3 to 29. Among those with baseline eGFR &lt;60 ml\/min per 1.73 m\n                      <jats:sup>2<\/jats:sup>\n                      , a higher level of tetrahydrocortisol sulfate was associated with lower risk of progression (hazard ratio, 0.8; 95% confidence interval, 0.7 to 0.9).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Untargeted plasma metabolomic profiling facilitated discovery of novel metabolite associations with CKD progression in children that were independent of established clinical predictors and highlight the role of select biologic pathways.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.2215\/cjn.00220121","type":"journal-article","created":{"date-parts":[[2021,8,6]],"date-time":"2021-08-06T15:25:12Z","timestamp":1628263512000},"page":"1178-1189","update-policy":"https:\/\/doi.org\/10.1097\/lww.0000000000001000","source":"Crossref","is-referenced-by-count":41,"title":["Metabolite Biomarkers of CKD Progression in Children"],"prefix":"10.2215","volume":"16","author":[{"given":"Michelle R.","family":"Denburg","sequence":"first","affiliation":[{"name":"Division of Nephrology, The Children\u2019s Hospital of Philadelphia, Philadelphia, Pennsylvania"},{"name":"Department of Pediatrics, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania"},{"name":"Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania"},{"name":"Center for Pediatric Clinical Effectiveness, The Children's Hospital of Philadelphia, Philadelphia, Pennsylvania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5946-2272","authenticated-orcid":false,"given":"Yunwen","family":"Xu","sequence":"additional","affiliation":[{"name":"Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alison G.","family":"Abraham","sequence":"additional","affiliation":[{"name":"Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4598-0669","authenticated-orcid":false,"given":"Josef","family":"Coresh","sequence":"additional","affiliation":[{"name":"Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jingsha","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Morgan E.","family":"Grams","sequence":"additional","affiliation":[{"name":"Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Harold I.","family":"Feldman","sequence":"additional","affiliation":[{"name":"Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania"},{"name":"Department of Medicine, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Paul L.","family":"Kimmel","sequence":"additional","affiliation":[{"name":"National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, Maryland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Casey M.","family":"Rebholz","sequence":"additional","affiliation":[{"name":"Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Eugene P.","family":"Rhee","sequence":"additional","affiliation":[{"name":"Department of Medicine, Massachusetts General Hospital, Department of Medicine, Harvard University, Boston, Massachusetts"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ramachandran S.","family":"Vasan","sequence":"additional","affiliation":[{"name":"Department of Medicine, Boston University School of Medicine, Boston University School of Public Health, and Boston University Center for Computing and Data Science, Boston, Massachusetts"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bradley A.","family":"Warady","sequence":"additional","affiliation":[{"name":"Children\u2019s Mercy Kansas City, Department of Pediatrics, University of Missouri-Kansas City School of Medicine, Kansas City, Missouri"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Susan L.","family":"Furth","sequence":"additional","affiliation":[{"name":"Division of Nephrology, The Children\u2019s Hospital of Philadelphia, Philadelphia, Pennsylvania"},{"name":"Department of Pediatrics, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania"},{"name":"Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"name":"for the CKD Biomarkers 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