{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,12]],"date-time":"2026-10-12T20:31:22Z","timestamp":1791837082002,"version":"4.3.4"},"reference-count":54,"publisher":"Ovid Technologies (Wolters Kluwer Health)","issue":"2","funder":[{"DOI":"10.13039\/100000009","name":"Foundation for the National Institutes of Health","doi-asserted-by":"publisher","award":["U01DK106982"],"award-info":[{"award-number":["U01DK106982"]}],"id":[{"id":"10.13039\/100000009","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000062","name":"National Institute of Diabetes and Digestive and Kidney Diseases","doi-asserted-by":"crossref","award":["P50DK114786"],"award-info":[{"award-number":["P50DK114786"]}],"id":[{"id":"10.13039\/100000062","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["lww.com","ovid.com"],"crossmark-restriction":true},"short-container-title":["JASN"],"published-print":{"date-parts":[[2022,2]]},"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Untargeted plasma metabolomic profiling combined with machine learning (ML) may lead to discovery of metabolic profiles that inform our understanding of pediatric CKD causes. We sought to identify metabolomic signatures in pediatric CKD based on diagnosis: FSGS, obstructive uropathy (OU), aplasia\/dysplasia\/hypoplasia (A\/D\/H), and reflux nephropathy (RN).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      Untargeted metabolomic quantification (GC-MS\/LC-MS, Metabolon) was performed on plasma from 702 Chronic Kidney Disease in Children study participants (\n                      <jats:italic toggle=\"yes\">n<\/jats:italic>\n                      : FSGS=63, OU=122, A\/D\/H=109, and RN=86). Lasso regression was used for feature selection, adjusting for clinical covariates. Four methods were then applied to stratify significance: logistic regression, support vector machine, random forest, and extreme gradient boosting. ML training was performed on 80% total cohort subsets and validated on 20% holdout subsets. Important features were selected based on being significant in at least two of the four modeling approaches. We additionally performed pathway enrichment analysis to identify metabolic subpathways associated with CKD cause.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>ML models were evaluated on holdout subsets with receiver-operator and precision-recall area-under-the-curve, F1 score, and Matthews correlation coefficient. ML models outperformed no-skill prediction. Metabolomic profiles were identified based on cause. FSGS was associated with the sphingomyelin-ceramide axis. FSGS was also associated with individual plasmalogen metabolites and the subpathway. OU was associated with gut microbiome\u2013derived histidine metabolites.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>ML models identified metabolomic signatures based on CKD cause. Using ML techniques in conjunction with traditional biostatistics, we demonstrated that sphingomyelin-ceramide and plasmalogen dysmetabolism are associated with FSGS and that gut microbiome\u2013derived histidine metabolites are associated with OU.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1681\/asn.2021040538","type":"journal-article","created":{"date-parts":[[2022,1,11]],"date-time":"2022-01-11T14:39:24Z","timestamp":1641911964000},"page":"375-386","update-policy":"https:\/\/doi.org\/10.1097\/lww.0000000000001000","source":"Crossref","is-referenced-by-count":59,"title":["Using Machine Learning to Identify Metabolomic Signatures of Pediatric Chronic Kidney Disease Etiology"],"prefix":"10.1681","volume":"33","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4969-3632","authenticated-orcid":false,"given":"Arthur M.","family":"Lee","sequence":"first","affiliation":[{"name":"Division of Nephrology, The Children\u2019s Hospital of Philadelphia, Philadelphia, 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University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania"}],"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":"Casey","family":"Rebholz","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":"Eugene P.","family":"Rhee","sequence":"additional","affiliation":[{"name":"Department of Medicine, Massachusetts General Hospital, Harvard University, Boston, Massachusetts"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4572-2608","authenticated-orcid":false,"given":"Harold I.","family":"Feldman","sequence":"additional","affiliation":[{"name":"Center for Clinical Epidemiology and Biostatistics, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Vasan S.","family":"Ramachandran","sequence":"additional","affiliation":[{"name":"Department of Medicine, Boston University School of Medicine, Boston University School of Public Health, Boston University Center for Computing and Data Science, Boston, Massachusetts"}],"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"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1812-6587","authenticated-orcid":false,"given":"Bradley A.","family":"Warady","sequence":"additional","affiliation":[{"name":"Department of Pediatrics, Children\u2019s Mercy Hospital, 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, University of Pennsylvania School of Medicine, Philadelphia, Pennsylvania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Michelle 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