Smith–Magenis syndrome (SMS) is a complex neurobehavioral disorder caused by haploinsufficiency of the retinoic acid-induced 1 (RAI1) gene on chromosome 17p11.2. Diagnostic strategies include molecular identification of a 17p11.2 microdeletion encompassing RAI1 or a mutation in RAI1. G-banding and fluorescent in situ hybridization (FISH) are the classical methods used to detect the SMS deletions, while multiplex ligation-dependent probe amplification (MLPA) and real-time quantitative PCR are the newer, cost-effective, and high-throughput technologies. Most SMS features are due to RAI1 haploinsufficiency, while the variability and severity of the disorder are modified by other genes in the 17p11.2 region. The functional role for RAI1 is not completely understood, but it is likely involved in transcription, based on homology and preliminary studies. Management of SMS is primarily a multidisciplinary approach and involves treatment for sleep disturbance, speech and occupational therapies, minor medical interventions, and management of behaviors.

Global metabolic profiling currently achievable by untargeted mass spectrometry‐based metabolomic platforms has great potential to advance our understanding of human disease states, including potential utility in the detection of novel and known inborn errors of metabolism (IEMs). There are few studies of the technical reproducibility, data analysis methods, and overall diagnostic capabilities when this technology is applied to clinical specimens for the diagnosis of IEMs. We explored the clinical utility of a metabolomic workflow capable of routinely generating semi‐quantitative z‐score values for ~900 unique compounds, including ~500 named human analytes, in a single analysis of human plasma. We tested the technical reproducibility of this platform and applied it to the retrospective diagnosis of 190 individual plasma samples, 120 of which were collected from patients with a confirmed IEM. Our results demonstrate high intra‐assay precision and linear detection for the majority compounds tested. Individual metabolomic profiles provided excellent sensitivity and specificity for the detection of a wide range of metabolic disorders and identified novel biomarkers for some diseases. With this platform, it is possible to use one test to screen for dozens of IEMs that might otherwise require ordering multiple unique biochemical tests. However, this test may yield false negative results for certain disorders that would be detected by a more well‐established quantitative test and in its current state should be considered a supplementary test. Our findings describe a novel approach to metabolomic analysis of clinical specimens and demonstrate the clinical utility of this technology for prospective screening of IEMs.