Targeted urine metabolomics with a graphical reporting tool for rapid diagnosis of inborn errors of metabolism.

The current diagnostic work-up of inborn errors of metabolism (IEM) is rapidly moving towards integrative analytical approaches. We aimed to develop an innovative, targeted urine metabolomics (TUM) screening procedure to accelerate the diagnosis of patients with IEM. Urinary samples, spiked with 3 stable isotope-labelled internal standards, were analysed for 258 diagnostic metabolites with an UHPLC-QTOF-MS configuration run in positive and negative ESI modes. The software automatically annotated peaks, corrected for peak overloading, and reported peak quality and shifting. Robustness and reproducibility were satisfactory for most metabolites. Z-scores were calculated against 4 age-group matched control cohorts. Disease phenotypes were scored based on database metabolite matching. Graphical reports comprised a needle plot, annotating abnormal metabolites, and a heatmap showing the prioritised disease phenotypes. In the clinical validation we analysed samples of 289 patients covering 78 OMIM phenotypes from 12 of the 15 SSIEM disease groups. The disease groups include disorders in the metabolism of amino acids, fatty acids, ketones, purines and pyrimidines, carbohydrates, porphyrias, neurotransmitters, vitamins, cofactors, and creatine. The reporting tool easily and correctly diagnosed most samples. Even subtle aberrant metabolite patterns as seen in mild multiple acyl-CoA dehydrogenase deficiency (, GAII) and maple syrup urine disease (MSUD) were correctly called without difficulty. Others, like creatine transporter deficiency, are illustrative of IEM that remain difficult to diagnose. We present TUM as a powerful diagnostic screening tool that that merges most urinary diagnostic assays expediting the diagnostics for patients suspected of an IEM. This article is protected by copyright. All rights reserved.