Precision of a Clinical Metabolomics Profiling Platform for Use in the Identification of Inborn Errors of Metabolism Lisa Ford, a Adam D. Kennedy, a Kelli D. Goodman, a Kirk L. Pappan, a Anne M. Evans, a Luke A.D. Miller, a Jacob E. Wulff, a,† Bobby R. Wiggs III, a John J. Lennon, a Sarah Elsea, b and Douglas R. Toal a,* Background: The application of whole-exome sequencing for the diagnosis of genetic disease has paved the way for systems-based approaches in the clinical laboratory. Here, we describe a clinical metabolomics method for the screening of metabolic diseases through the analysis of a multi-pronged mass spectrometry platform. By simultaneously measuring hundreds of metabolites in a single sample, clinical metabolomics offers a comprehensive approach to identify metabolic perturbations across multiple biochemical pathways. Methods: We conducted a single- and multi-day precision study on hundreds of metabolites in human plasma on 4, multi-arm, high-throughput metabolomics platforms. Results: The average laboratory coefficient of variation (CV) on the 4 platforms was between 9.3 and 11.5% (me- dian, 6.5–8.4%), average inter-assay CV on the 4 platforms ranged from 9.9 to 12.6% (median, 7.0–8.3%) and aver- age intra-assay CV on the 4 platforms ranged from 5.7 to 6.9% (median, 3.5–4.4%). In relation to patient sample testing, the precision of multiple biomarkers associated with IEM disorders showed CVs that ranged from 0.2 to 11.0% across 4 analytical batches. Conclusions: This evaluation describes single and multi-day precision across 4 identical metabolomics plat- forms, comprised each of 4 independent method arms, and reproducibility of the method for the measurement of key IEM metabolites in patient samples across multiple analytical batches, providing evidence that the method is robust and reproducible for the screening of patients with inborn errors of metabolism. IMPACT STATEMENT Inborn errors of metabolism affect the pediatric patient population through severe medical and physical detriments. Clinical metabolomics shows utility as a diagnostic tool for the identification and relative quanti- tation of a broad range of analytes associated with metabolic disorders. We show that a metabolomics ap- proach using multiple LC–MS/MS methods is precise and reproducible in measuring biomarkers associated with the diagnosis of inborn errors of metabolism. a Metabolon Inc., Morrisville, NC; b Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX. *Address correspondence to this author at: Metabolon, 617 Davis Dr. Suite 400, Morrisville, NC 27560. Fax 919-287-2348; E-mail dtoal@metabolon.com. † Present address: Synteract, Carlsbad, CA 92010. Received March 12, 2019; accepted September 9, 2019. DOI: 10.1093/jalm/jfz026 V C American Association for Clinical Chemistry 2020. All rights reserved. For permissions, please email: journals.permissions@oup.com. ........................................................................................................ 342 JALM | 342–356 | 05:02 | March 2020 ARTICLE Downloaded from https://academic.oup.com/jalm/article/5/2/342/5741398 by guest on 05 January 2023