Inborn errors of metabolism (IEM) are rare genetic disorders that cause alterations or deficiencies in enzymes involved in metabolism. As a result, metabolic pathways are disrupted, and the body cannot properly metabolize food into energy. Furthermore, the disruption in metabolism can cause substrate accumulation resulting in moderate to severe symptoms, including life-long disability and death. IEM’s typically present in early childhood but can become symptomatic at any age.

Individually, IEM’s are rare but collectively they are numerous with over 500 identified human disorders contributing to an incidence of approximately 1 in 1,000 births. In the U.S., the number of disorders listed in newborn screening panels includes 34 core conditions, with some states screening for as many as 58 conditions. Yet, as stated, IEM’s number in the hundreds and are difficult to detect due to wide and overlapping clinical spectrum. Consequently, the application of global or untargeted analytical approaches, where a priori assumption of the affected metabolic pathway is not required, provides advantages over targeted analytical approaches and can extend the diagnostic yield of IEM screens beyond the 34-58 disorders that are currently evaluated in newborn screening programs, especially in complex cases.

Clinical metabolomics is emerging as a tool for IEM screening. The goal of Metabolon’s approach to metabolomics in this setting is to measure the entire small molecule metabolite content of a patient sample with comparison to a control reference population to identify biochemical pathway disturbance. Recently we described the precision and reproducibility of a clinical metabolomic method that measures hundreds of metabolites in individual patient samples (i.e., N-of-1) for the identification of IEM’s (1).

Since the intent of clinical metabolomics is to measure hundreds of metabolites in a single plasma sample, it is not practical to use traditional LC-MS/MS isotope dilution methods to perform absolute quantitation of individual analytes. Rather, in clinical metabolomics, a relative-quantitation approach is used where the raw LC-MS/MS values of individual analytes in a patient EDTA plasma sample are normalized against replicate EDTA plasma samples to correct for daily variation. The normalized values for each analyte in the patient sample is then compared to an identically normalized reference population to produce a z-score. The advantages and limitations of this novel approach to clinical metabolomics is described in detail in Ford, et. al.

The application of clinical metabolomics to the identification of IEM’s, and in clinical laboratory medicine in general, is beginning to show signs of significant potential (2). In addition to screening for IEM’s, the method has been applied to aid in the interpretation of variance of unknown significance findings from DNA sequencing and to discover and track the effects of clinical treatment. Additional studies are needed to further characterize the limitations, performance and utility of the method in clinical laboratory medicine.


  1. Ford, L., Kennedy, A.D., Goodman, K.D., Pappan, K.L., Evans, A.M., Miller, L.A.D., et. al. Precision of a clinical metabolomics profiling platform for use in the identification of inborn errors of metabolism. J. App. Lab Med. 2020; 5
  2. Kennedy, A.D., Wittmann, B.M., Evans, A.M., Miller, L.A.D., Toal, D.R., Lonergan, S., et. al., Metabolomics in the clinic: A review of the shared and unique features of untargeted metabolomics for clinical research and clinical testing. J. Mass Spectrometry 2018; 53:1036-1154.