Global metabolomic profiling of patient cohorts is a powerful tool for the discovery of diagnostic biomarkers. Recently we have conducted translational research to extend the application of metabolomics from population studies and biomarker discovery to individual patient testing (N-of-one studies) for the detection of Inborn Errors on Metabolism (IEM). Here, I will briefly summarize our experience with clinical metabolomics and explain how we believe that this global approach holds great promise for individual patient testing as a supplemental tool for IEM diagnosis.

Metabolomics is the global interrogation of the biochemical components (i.e., small molecular weight biochemicals or metabolites < 1,500 Da) in a biological sample, and the metabolome is a measure of the output of biochemical pathways. Current analytical platforms in the clinical laboratory provide snapshots of individual metabolite levels and as such, only provide a partial view of the metabolic fingerprint. The promise of metabolomics, and incidentally, its major challenge, has been to develop a technology that can extract, identify, and quantitate the entire spectrum of small molecules in a biological sample. By interrogating the entire biochemical spectrum of a clinical sample it is possible to identify meaningful patterns for multiple analytes, spanning diverse and inter-related metabolic pathways.

Achievements in metabolomics have been driven in recent years by advances in mass spectrometry and the application of advanced multisystem approaches where the best separation and detection instrument technologies are developed to run in tandem. For example, we have developed and described a method in which a sample extract is split into four aliquots and run on three ultra-high-performance liquid chromatography (UHPLC) methods that are enhanced for the detection of polar and charged compounds and a fourth aliquot is run by gas chromatography.  Following mass spectrometry, a suite of software methods automates the detection of separated compounds using retention time, mass spectral and mass fragmentation signature information to identify each compound. Once the compound is identified, the strongest ion signal from the four arms of the platform is used to determine a relative concentration for each compound in the sample.

Recently we have reported on the analytical validity of our global metabolomics workflow which is capable of routinely generating semi-quantitative z-score values for over 1,000 unique compounds, including over 700 named human analytes, in a single analysis of human plasma.  Among other criteria, this method has been validated for precision, linearity, carryover, LOD, interference, and stability. Accuracy of the method was established on a set of 200 pediatric plasma samples (130 samples from patients with 30 known IEMs and 70 samples from healthy individuals) and correctly identified 29 of the 30 disorders. We have also demonstrated utility of the method in urine and CSF samples, and to date we have shown that our global metabolomics approach can correctly identify disease signatures associated with at least 47 IEMs.

Multiple specimen types and analytical approaches are currently required to screen for the long list of known IEMs. Our work has demonstrated that it is possible to use one blood plasma sample to screen for multiple IEMs that otherwise require an array of targeted biochemical tests. Furthermore, since current IEM triage workflows only test for a limited number of disorders, it seems clear that the global approach provides a more encompassing strategy that will reduce the number of affected patients who remain undiagnosed due to limitations of standard diagnostic approaches.


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