<HashMap><database>biostudies-literature</database><scores/><additional><submitter>Denburg MR</submitter><funding>NIDDK NIH HHS</funding><funding>NIDDK</funding><pagination>1178-1189</pagination><full_dataset_link>https://www.ebi.ac.uk/biostudies/studies/S-EPMC8455058</full_dataset_link><repository>biostudies-literature</repository><omics_type>Unknown</omics_type><volume>16(8)</volume><pubmed_abstract>&lt;h4>Background and objectives&lt;/h4>Metabolomics facilitates the discovery of biomarkers and potential therapeutic targets for CKD progression.&lt;h4>Design, setting, participants, &amp; measurements&lt;/h4>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 </pubmed_abstract><journal>Clinical journal of the American Society of Nephrology : CJASN</journal><pubmed_title>Metabolite Biomarkers of CKD Progression in Children.</pubmed_title><pmcid>PMC8455058</pmcid><funding_grant_id>U01DK085689</funding_grant_id><funding_grant_id>U01 DK106981</funding_grant_id><funding_grant_id>U01DK103225</funding_grant_id><funding_grant_id>U01DK106982</funding_grant_id><funding_grant_id>U01 DK103225</funding_grant_id><funding_grant_id>U01 DK106982</funding_grant_id><funding_grant_id>U01 DK066174</funding_grant_id><funding_grant_id>U01 DK085689</funding_grant_id><funding_grant_id>U01 DK066143</funding_grant_id><funding_grant_id>U24 DK066116</funding_grant_id><funding_grant_id>K23 DK093556</funding_grant_id><funding_grant_id>K23DK093556</funding_grant_id><pubmed_authors>Abraham A</pubmed_authors><pubmed_authors>Coca S</pubmed_authors><pubmed_authors>Sarnak M</pubmed_authors><pubmed_authors>Kimmel PL</pubmed_authors><pubmed_authors>Tin A</pubmed_authors><pubmed_authors>Shou H</pubmed_authors><pubmed_authors>Dubin R</pubmed_authors><pubmed_authors>Levey AS</pubmed_authors><pubmed_authors>Foster M</pubmed_authors><pubmed_authors>Xie D</pubmed_authors><pubmed_authors>Rebholz CM</pubmed_authors><pubmed_authors>Rovin B</pubmed_authors><pubmed_authors>Ix J</pubmed_authors><pubmed_authors>Furth S</pubmed_authors><pubmed_authors>Shlipak MG</pubmed_authors><pubmed_authors>Vasan RS</pubmed_authors><pubmed_authors>Chen J</pubmed_authors><pubmed_authors>Gossett D</pubmed_authors><pubmed_authors>Waikar S</pubmed_authors><pubmed_authors>Clish C</pubmed_authors><pubmed_authors>Hostetter T</pubmed_authors><pubmed_authors>Gutierrez OM</pubmed_authors><pubmed_authors>Sabbisetti V</pubmed_authors><pubmed_authors>Abraham AG</pubmed_authors><pubmed_authors>Xu Y</pubmed_authors><pubmed_authors>Inker LA</pubmed_authors><pubmed_authors>Warady B</pubmed_authors><pubmed_authors>Ganz P</pubmed_authors><pubmed_authors>Rebholz C</pubmed_authors><pubmed_authors>Rhee E</pubmed_authors><pubmed_authors>Deo R</pubmed_authors><pubmed_authors>Ramachandran VS</pubmed_authors><pubmed_authors>Bonventre J</pubmed_authors><pubmed_authors>Feldman HI</pubmed_authors><pubmed_authors>Parikh C</pubmed_authors><pubmed_authors>Mifflin T</pubmed_authors><pubmed_authors>Seegmiller J</pubmed_authors><pubmed_authors>McMahon G</pubmed_authors><pubmed_authors>Nadkarni GN</pubmed_authors><pubmed_authors>Greenberg J</pubmed_authors><pubmed_authors>Rhee EP</pubmed_authors><pubmed_authors>Furth SL</pubmed_authors><pubmed_authors>CKD Biomarkers Consortium</pubmed_authors><pubmed_authors>Grams M</pubmed_authors><pubmed_authors>Ferket BS</pubmed_authors><pubmed_authors>Anderson A</pubmed_authors><pubmed_authors>Whitehead K</pubmed_authors><pubmed_authors>Schelling J</pubmed_authors><pubmed_authors>Grams ME</pubmed_authors><pubmed_authors>Ballard S</pubmed_authors><pubmed_authors>Warady BA</pubmed_authors><pubmed_authors>Massaro J</pubmed_authors><pubmed_authors>Klein J</pubmed_authors><pubmed_authors>Denburg M</pubmed_authors><pubmed_authors>Coresh J</pubmed_authors><pubmed_authors>Denburg MR</pubmed_authors><pubmed_authors>Collins H</pubmed_authors></additional><is_claimable>false</is_claimable><name>Metabolite Biomarkers of CKD Progression in Children.</name><description>&lt;h4>Background and objectives&lt;/h4>Metabolomics facilitates the discovery of biomarkers and potential therapeutic targets for CKD progression.&lt;h4>Design, setting, participants, &amp; measurements&lt;/h4>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 </description><dates><release>2021-01-01T00:00:00Z</release><publication>2021 Aug</publication><modification>2025-04-26T16:30:25.818Z</modification><creation>2025-04-06T15:12:11.018Z</creation></dates><accession>S-EPMC8455058</accession><cross_references><pubmed>34362785</pubmed><doi>10.2215/cjn.00220121</doi><doi>10.2215/CJN.00220121</doi></cross_references></HashMap>