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Automated Tandem Mass Spectrometry for Mass Newborn Screening for Disorders in Fatty Acid, Organic Acid, and Amino Acid Metabolism

Naylor, Edwin W., Chace, Donald H.·1999·Journal of Child Neurology

Abstract

Development of acylcarnitine and amino acid profiling using tandem mass spectrometry, and its application for use with dried blood specimens collected on filter-paper cards, has introduced an innovative new technology for detecting inborn errors of fatty acid, organic acid, and amino acid metabolism. From November 1, 1992 through June 30, 1999 we screened more than 700,000 newborns in Pennsylvania, Ohio, North Carolina, and Louisiana. We have prospectively detected 163 inborn errors of metabolism. Eighty-six patients have amino acid metabolism errors. Among them are phenylketonuria, hyperphenylalaninemia, maple syrup urine disease, and several urea cycle disorders. Thirty-two have organic acid metabolism errors, including glutaric aciduria type 1; 3-methylcrotonyl coenzyme A (CoA) carboxylase deficiency, propionic acidemia, methylmalonic acidemia, and 3-hydroxy-3-methylglutaryl-CoA lyase deficiency; and 45 have fatty acid oxidation errors, including 36 with medium-chain acyl-CoA dehydrogenase deficiency. Details of the methodology are presented and the potential of this screening technology is discussed. (J Child Neurol 1999;14(Suppl 1):S4-S8).

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Metadata

Authors
Naylor, E. W., & Chace, D. H.
Venue
Journal of Child Neurology
Published
1999-11
Volume
14
Issue
1_suppl
Pages
S4-S8
ISSN
  • 0883-0738 (ISSN-L)
  • 1708-8283
OpenAlex
W1593848483
Cited by
167
References
0
FWCI
6.96
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Automated Tandem Mass Spectrometry for Mass Newborn Screening for Disorders in Fatty Acid, Organic Acid, and Amino Acid Metabolism. https://app.cochat.ai/papers/10.1177/0883073899014001021
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Naylor, E. W., & Chace, D. H. (1999). Automated Tandem Mass Spectrometry for Mass Newborn Screening for Disorders in Fatty Acid, Organic Acid, and Amino Acid Metabolism. Journal of Child Neurology. https://doi.org/10.1177/0883073899014001021

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Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.