{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,27]],"date-time":"2026-08-27T21:54:52Z","timestamp":1787867692761,"version":"build-2784847793"},"reference-count":40,"publisher":"Wiley","issue":"2","license":[{"start":{"date-parts":[[2023,2,2]],"date-time":"2023-02-02T00:00:00Z","timestamp":1675296000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000071","name":"National Institute of Child Health and Human Development","doi-asserted-by":"publisher","award":["R01HD102537"],"award-info":[{"award-number":["R01HD102537"]}],"id":[{"id":"10.13039\/100000071","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["J of Inher Metab Disea"],"published-print":{"date-parts":[[2023,3]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Improved second\u2010tier assays are needed to reduce the number of false positives in newborn screening (NBS) for inherited metabolic disorders including those on the Recommended Uniform Screening Panel (RUSP). We developed an expanded metabolite panel for second\u2010tier testing of dried blood spot (DBS) samples from screen\u2010positive cases reported by the California NBS program, consisting of true\u2010 and false\u2010positives from four disorders: glutaric acidemia type I (GA1), methylmalonic acidemia (MMA), ornithine transcarbamylase deficiency (OTCD), and very long\u2010chain acyl\u2010CoA dehydrogenase deficiency (VLCADD). This panel was assembled from known disease markers and new features discovered by untargeted metabolomics and applied to second\u2010tier analysis of single DBS punches using liquid chromatography\u2013tandem mass spectrometry (LC\u2013MS\/MS) in a 3\u2010min run. Additionally, we trained a Random Forest (RF) machine learning classifier to improve separation of true\u2010 and false positive cases. Targeted metabolomic analysis of 121 analytes from DBS extracts in combination with RF classification at a sensitivity of 100% reduced false positives for GA1 by 83%, MMA by 84%, OTCD by 100%, and VLCADD by 51%. This performance was driven by a combination of known disease markers (3\u2010hydroxyglutaric acid, methylmalonic acid, citrulline, and C14:1), other amino acids and acylcarnitines, and novel metabolites identified to be isobaric to several long\u2010chain acylcarnitine and hydroxy\u2010acylcarnitine species. These findings establish the effectiveness of this second\u2010tier test to improve screening for these four conditions and demonstrate the utility of supervised machine learning in reducing false\u2010positives for conditions lacking clearly discriminating markers, with future studies aimed at optimizing and expanding the panel to additional disease targets.<\/jats:p>","DOI":"10.1002\/jimd.12591","type":"journal-article","created":{"date-parts":[[2023,1,21]],"date-time":"2023-01-21T09:44:54Z","timestamp":1674294294000},"page":"194-205","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["Validation of a targeted metabolomics panel for improved second\u2010tier newborn screening"],"prefix":"10.1002","volume":"46","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6129-5859","authenticated-orcid":false,"given":"Justin","family":"Mak","sequence":"first","affiliation":[{"name":"Clinical Biochemical Genetics Laboratory, 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