Validation of a targeted metabolomics panel for improved second‐tier newborn screening

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.

[1]  M. Godejohann,et al.  Identification of potential interferents of methylmalonic acid: a previously unrecognized pitfall in clinical diagnostics and newborn screening. , 2022, Clinical biochemistry.

[2]  P. Chakraborty,et al.  Biotinidase Activity is Affected by Both Seasonal Temperature and Filter Collection Cards. , 2022, Clinical biochemistry.

[3]  S. Kölker,et al.  Opportunities and challenges in machine learning‐based newborn screening—A systematic literature review , 2022, JIMD reports.

[4]  M. Caggana,et al.  Method modification to reduce false positives for newborn screening of guanidinoacetate methyltransferase deficiency. , 2022, Molecular genetics and metabolism.

[5]  Marcus J. Miller,et al.  A clinically validated method to separate and quantify underivatized acylcarnitines and carnitine metabolic intermediates using mixed-mode chromatography with tandem mass spectrometry , 2021, Journal of Chromatography A.

[6]  Brian D. Williamson,et al.  Improving random forest predictions in small datasets from two-phase sampling designs , 2021, BMC Medical Informatics and Decision Making.

[7]  H. Nakajima,et al.  Development of Second-Tier Liquid Chromatography-Tandem Mass Spectrometry Analysis for Expanded Newborn Screening in Japan , 2021, International journal of neonatal screening.

[8]  J. Xia,et al.  MetaboAnalyst 5.0: narrowing the gap between raw spectra and functional insights , 2021, Nucleic Acids Res..

[9]  Hongyu Zhao,et al.  Timing of Newborn Blood Collection Alters Metabolic Disease Screening Performance , 2021, Frontiers in Pediatrics.

[10]  Junjuan Wang,et al.  Determination of methylmalonic acid, 2-methylcitric acid, and total homocysteine in dried blood spots by liquid chromatography–tandem mass spectrometry: A reliable follow-up method for propionylcarnitine-related disorders in newborn screening , 2020, Journal of medical screening.

[11]  Hongyu Zhao,et al.  Ethnic variability in newborn metabolic screening markers associated with false‐positive outcomes , 2020, Journal of inherited metabolic disease.

[12]  T. Cowan,et al.  Metabolic profiling by reversed-phase/ion-exchange mass spectrometry. , 2020, Journal of chromatography. B, Analytical technologies in the biomedical and life sciences.

[13]  Hongyu Zhao,et al.  Reducing False-Positive Results in Newborn Screening Using Machine Learning , 2020, International journal of neonatal screening.

[14]  D. Hougaard,et al.  Danish expanded newborn screening is a successful preventive public health programme. , 2020, Danish medical journal.

[15]  Aleksandar Milosavljevic,et al.  Untargeted Metabolomic Profiling Reveals Multiple Pathway Perturbations and New Clinical Biomarkers in Urea Cycle Disorders , 2019, Genetics in Medicine.

[16]  P. Rinaldo,et al.  Precision newborn screening for lysosomal disorders , 2018, Genetics in Medicine.

[17]  P. Rinaldo,et al.  Moonlighting newborn screening markers: the incidental discovery of a second-tier test for Pompe disease , 2017, Genetics in Medicine.

[18]  B. Williams,et al.  Integrated Metabolomics Assessment of Human Dried Blood Spots and Urine Strips , 2017, Metabolites.

[19]  L. Prosser,et al.  Evaluating Harms in the Assessment of Net Benefit: A Framework for Newborn Screening Condition Review , 2016, Maternal and Child Health Journal.

[20]  A. Spitzer,et al.  Gestational Age and Age at Sampling Influence Metabolic Profiles in Premature Infants , 2014, Pediatrics.

[21]  Piero Rinaldo,et al.  Postanalytical tools improve performance of newborn screening by tandem mass spectrometry , 2014, Genetics in Medicine.

[22]  J. Golbahar,et al.  Short-Term Stability of Amino acids and Acylcarnitines in the Dried Blood Spots Used to Screen Newborns for Metabolic Disorders , 2014, Journal of medical screening.

[23]  X. Fang,et al.  Separation and identification of underivatized plasma acylcarnitine isomers using liquid chromatography-tandem mass spectrometry for the differential diagnosis of organic acidemias and fatty acid oxidation defects. , 2013, Journal of chromatography. A.

[24]  Sheau-Ling Hsieh,et al.  Web-Based Newborn Screening System for Metabolic Diseases: Machine Learning Versus Clinicians , 2013, Journal of medical Internet research.

[25]  Vagelis Papakonstantinou,et al.  Enhanced interpretation of newborn screening results without analyte cutoff values , 2012, Genetics in Medicine.

[26]  S. Terry,et al.  The impact of false-positive newborn screening results on families: a qualitative study , 2012, Genetics in Medicine.

[27]  P. Chakraborty,et al.  Diagnosis of glutaric aciduria type 1 by measuring 3-hydroxyglutaric acid in dried urine spots by liquid chromatography tandem mass spectrometry , 2011, Journal of Inherited Metabolic Disease.

[28]  B. Casetta,et al.  Progress in expanded newborn screening for metabolic conditions by LC-MS/MS in Tuscany: Update on methods to reduce false tests , 2008, Journal of Inherited Metabolic Disease.

[29]  C. Hoppel,et al.  Quantification of carnitine and acylcarnitines in biological matrices by HPLC electrospray ionization-mass spectrometry. , 2008, Clinical chemistry.

[30]  K. Strauss,et al.  Second-tier test for quantification of alloisoleucine and branched-chain amino acids in dried blood spots to improve newborn screening for maple syrup urine disease (MSUD). , 2008, Clinical chemistry.

[31]  P. Rinaldo,et al.  Reduction of the false-positive rate in newborn screening by implementation of MS/MS-based second-tier tests: The Mayo Clinic experience (2004–2007) , 2007, Journal of Inherited Metabolic Disease.

[32]  Dimitri A Christakis,et al.  State Newborn Screening in the Tandem Mass Spectrometry Era: More Tests, More False-Positive Results , 2006, Pediatrics.

[33]  Christian Böhm,et al.  Supervised machine learning techniques for the classification of metabolic disorders in newborns , 2004, Bioinform..

[34]  Martin Lindner,et al.  Expanded newborn screening for inborn errors of metabolism by electrospray ionization-tandem mass spectrometry: results, outcome, and implications. , 2003, Pediatrics.

[35]  L. Breiman.  Random Forests , 2001, Encyclopedia of Machine Learning and Data Mining.

[36]  J. Bonham,et al.  Introduction of a Simple Second Tier Screening Test for C5 Isobars in Dried Blood Spots: Reducing the False Positive Rate for Isovaleric Acidaemia in Expanded Newborn Screening. , 2018, JIMD reports.

[37]  G. la Marca,et al.  Expanded newborn screening by mass spectrometry: New tests, future perspectives. , 2016, Mass spectrometry reviews.

[38]  J. Murray,et al.  Clinical and environmental influences on metabolic biomarkers collected for newborn screening. , 2013, Clinical biochemistry.