ArticleJournal of inherited metabolic disease2023
Validation of a targeted metabolomics panel for improved second-tier newborn screening.
Article in Journal of inherited metabolic disease, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
What it found
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Who cites it
12 citing papers in PubMed, 17 citations in OpenAlex.
- Emerging Quantitative Targeted Metabolomics Approaches for Future Inborn Errors of Metabolism Screening.Diagnostics (Basel, Switzerland) · 2026Review
- Untargeted LC-HRMS of Dried Blood Spots Reveals Metabolic Alterations and Candidate Biomarkers in Glutaric Aciduria Type-1.Metabolites · 2026Article
- Dried blood spots capture a wide range of metabolic pathways and biological changes associated with exercise.Metabolomics : Official journal of the Metabolomic Society · 2025Article
- Improving newborn screening accuracy through genome sequencing, targeted metabolomics, and machine learning.BMC medical genomics · 2025Article
- Using metabolomics to investigate the relationship between the metabolomic profile of the intestinal microbiota derivatives and mental disorders in inflammatory bowel diseases: a narrative review.Research in pharmaceutical sciences · 2025Review
- Improving the second-tier classification of methylmalonic acidemia patients using a machine learning ensemble method.World journal of pediatrics : WJP · 2024Article
- Targeted and untargeted metabolomics and lipidomics in dried blood microsampling: Recent applications and perspectives.Analytical science advances · 2024Review
- Current Status of Newborn Bloodspot Screening Worldwide 2024: A Comprehensive Review of Recent Activities (2020-2023).International journal of neonatal screening · 2024Review
- Harnessing Next-Generation Sequencing as a Timely and Accurate Second-Tier Screening Test for Newborn Screening of Inborn Errors of Metabolism.International journal of neonatal screening · 2024Article
- Revolutionizing Blood Collection: Innovations, Applications, and the Potential of Microsampling Technologies for Monitoring Metabolites and Lipids.Metabolites · 2024Review
- Specifications of the ACMG/AMP guidelines for ACADVL variant interpretation.Molecular genetics and metabolism · 2023Article
- Biochemical and molecular features of chinese patients with glutaric acidemia type 1 from Fujian Province, southeastern China.Orphanet journal of rare diseases · 2023Article
Corrections and comments
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Authors and funding
7 authors at 3 institutions in 1 country.
Funding
Abstract
Improved second-tier 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-tier testing of dried blood spot (DBS) samples from screen-positive cases reported by the California NBS program, consisting of true- and false-positives from four disorders: glutaric acidemia type I (GA1), methylmalonic acidemia (MMA), ornithine transcarbamylase deficiency (OTCD), and very long-chain acyl-CoA dehydrogenase deficiency (VLCADD). This panel was assembled from known disease markers and new features discovered by untargeted metabolomics and applied to second-tier analysis of single DBS punches using liquid chromatography-tandem mass spectrometry (LC-MS/MS) in a 3-min run. Additionally, we trained a Random Forest (RF) machine learning classifier to improve separation of true- 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-hydroxyglutaric acid, methylmalonic acid, citrulline, and C14:1), other amino acids and acylcarnitines, and novel metabolites identified to be isobaric to several long-chain acylcarnitine and hydroxy-acylcarnitine species. These findings establish the effectiveness of this second-tier test to improve screening for these four conditions and demonstrate the utility of supervised machine learning in reducing false-positives for conditions lacking clearly discriminating markers, with future studies aimed at optimizing and expanding the panel to additional disease targets.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.