{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,31]],"date-time":"2026-08-31T23:14:16Z","timestamp":1788218056370,"version":"build-2803163510"},"reference-count":25,"publisher":"SAGE Publications","issue":"9","license":[{"start":{"date-parts":[[2016,7,22]],"date-time":"2016-07-22T00:00:00Z","timestamp":1469145600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Genetic Testing and Molecular Biomarkers"],"published-print":{"date-parts":[[2016,9,1]]},"abstract":"<jats:p>\n                    <jats:italic toggle=\"yes\">Aims:<\/jats:italic>\n                    We wished to determine the efficacy of using urine as an analyte to screen for a broad range of metabolic products associated with multiple different types of inborn errors of metabolism (IEMs), using an automated mass spectrometry-based assay. Urine was compared with plasma samples from a similar cohort analyzed using the same assay. Specimens were analyzed using two different commonly utilized urine normalization methods based on creatinine and osmolality, respectively.\n                    <jats:italic toggle=\"yes\">Methods:<\/jats:italic>\n                    Biochemical profiles for each sample (from both affected and unaffected subjects) were obtained using a mass spectrometry-based platform and population-based statistical analyses.\n                    <jats:italic toggle=\"yes\">Results:<\/jats:italic>\n                    We identified over 1200 biochemicals from among 100 clinical urine samples and identified clear biochemical signatures for 16 of 18 IEM diseases tested. The two diseases that did not result in clear signatures, X-linked creatine transporter deficiency and ornithine transcarbamylase deficiency, were from individuals under treatment, which masked biomarker signatures. Overall the process variability and coefficient of variation for isolating and identifying biochemicals by running technical replicates of each urine sample was 10%.\n                    <jats:italic toggle=\"yes\">Conclusions:<\/jats:italic>\n                    A single urine sample analyzed with our integrated metabolomic platform can identify signatures of IEMs that are traditionally identified using many different assays and multiple sample types. Creatinine and osmolality-normalized data were robust to the detection of the disorders and samples tested here.\n                  <\/jats:p>","DOI":"10.1089\/gtmb.2015.0291","type":"journal-article","created":{"date-parts":[[2016,7,22]],"date-time":"2016-07-22T15:30:53Z","timestamp":1469201453000},"page":"485-495","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":52,"title":["Metabolomic Profiling of Human Urine as a Screen for Multiple Inborn Errors of Metabolism"],"prefix":"10.1177","volume":"20","author":[{"given":"Adam D.","family":"Kennedy","sequence":"first","affiliation":[{"name":"Metabolon, Inc."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Marcus J.","family":"Miller","sequence":"additional","affiliation":[{"name":"Baylor College of Medicine"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kirk","family":"Beebe","sequence":"additional","affiliation":[{"name":"Metabolon, Inc."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jacob E.","family":"Wulff","sequence":"additional","affiliation":[{"name":"Metabolon, Inc."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anne M.","family":"Evans","sequence":"additional","affiliation":[{"name":"Metabolon, Inc."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Luke A.D.","family":"Miller","sequence":"additional","affiliation":[{"name":"Metabolon, Inc."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"V. Reid","family":"Sutton","sequence":"additional","affiliation":[{"name":"Baylor College of Medicine"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qin","family":"Sun","sequence":"additional","affiliation":[{"name":"Baylor College of Medicine"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sarah H.","family":"Elsea","sequence":"additional","affiliation":[{"name":"Baylor College of Medicine"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2016,7,22]]},"reference":[{"key":"e_1_3_3_2_1","doi-asserted-by":"publisher","DOI":"10.1038\/ejcn.2013.79"},{"key":"e_1_3_3_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ymgme.2015.04.008"},{"key":"e_1_3_3_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ymgme.2008.07.001"},{"key":"e_1_3_3_5_1","first-page":"105","article-title":"Reference intervals for 24 laboratory parameters determined in 24-hour urine collections","volume":"54","author":"Curcio R","year":"2015","unstructured":"Curcio R, , Stettler H, , Suter PM, et al. (2015) Reference intervals for 24 laboratory parameters determined in 24-hour urine collections. Clin Chem Lab Med, 54:105-116.","journal-title":"Clin Chem Lab Med"},{"key":"e_1_3_3_6_1","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.1114410109"},{"key":"e_1_3_3_7_1","doi-asserted-by":"publisher","DOI":"10.1186\/1758-2946-2-9"},{"key":"e_1_3_3_8_1","doi-asserted-by":"publisher","DOI":"10.1021\/ac051632c"},{"key":"e_1_3_3_9_1","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.1118726109"},{"key":"e_1_3_3_10_1","doi-asserted-by":"publisher","DOI":"10.1021\/ac503190m"},{"key":"e_1_3_3_11_1","first-page":"11000132","article-title":"High resolution mass spectrometry improves data quantity and quality as compared to unit mass resolution mass spectrometry in high-throughput profiling metabolomics","volume":"4","author":"Evans AM","year":"2014","unstructured":"Evans AM, , Bridgewater BR, , Liu Q, et al. 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