Metabolomic Profiling of Human Urine as a Screen for Multiple Inborn Errors of Metabolism

Abstract

Aims: We wished to determine the efficacy of using urine as an analyte to screen for a broad range of metabolic products associated with multiple different types of inborn errors of metabolism (IEMs), using an automated mass spectrometry-based assay. Urine was compared with plasma samples from a similar cohort analyzed using the same assay. Specimens were analyzed using two different commonly utilized urine normalization methods based on creatinine and osmolality, respectively.

Methods: Biochemical profiles for each sample (from both affected and unaffected subjects) were obtained using a mass spectrometry-based platform and population-based statistical analyses.

Results: We identified over 1200 biochemicals from among 100 clinical urine samples and identified clear biochemical signatures for 16 of 18 IEM diseases tested. The two diseases that did not result in clear signatures, X-linked creatine transporter deficiency and ornithine transcarbamylase deficiency, were from individuals under treatment, which masked biomarker signatures. Overall the process variability and coefficient of variation for isolating and identifying biochemicals by running technical replicates of each urine sample was 10%.

Conclusions: A single urine sample analyzed with our integrated metabolomic platform can identify signatures of IEMs that are traditionally identified using many different assays and multiple sample types. Creatinine and osmolality-normalized data were robust to the detection of the disorders and samples tested here.

FIG. 1.

Dot plot analysis for individual biochemicals for all subjects diagnosed with an IEM and compared to the control population. The graph to the right depicts all biochemicals detected across the study and graphed as a function of Z scores for each of the clinical samples. Each biochemical is grouped into their respective family designations (e.g., amino acids, carbohydrates, and lipids) Z scores increase for each of the biochemicals as you proceed to the right. The graphs on the left show increased resolution for the amino acid and nucleotide families, respectively, and indicate the diseases, which could be identified by increased Z scores of the respective biochemicals. Urea cycle disorders (e.g., citrullinemia) can be identified by multiple groups of biochemicals given the relationship of different biological pathways (urea cycle and pyrimidine metabolism). IEMs, inborn errors of metabolism.

FIG. 2.

Biomarkers of IEMs in urine identified through global biochemical profiling. For each disease and biochemical listed, blue triangles represent each of the undiagnosed controls and red bars indicate the affected individuals diagnosed with that disease. The dotted lines represent the −2 and 2 Z-score cutoff for establishing relevance.

FIG. 3.

Biomarkers of citrullinemia and phenylbutyrate treatment. Biomarkers of disease (urea cycle and pyrimidine metabolism) and disease treatment (phenylbutyrate intervention) could be identified in a single urine sample.

FIG. 4.

Biochemical map showing metabolic perturbations in urine from a patient diagnosed with citrullinemia. Red circles indicate biochemicals with positive Z scores and blue circles indicate biochemicals with negative Z scores. The diameters of the circles indicate the magnitude of the Z score. Pink circles represent biochemicals with Z score of 1.5 ≤ Z < 2.0, and light blue circles represent biochemicals with −2.0 < Z ≤ −1.5. Black circles represent other biochemicals in the pathway detected in the urine sample, but had Z scores of −1.5 < Z < 1.5. Gray circles represent biochemicals in the library but not detected. White circles are biochemicals not detected on the mass spectrometry platform.

FIG. 5.

Creatinine and osmolality normalization of clinical urine samples. (A) Creatinine measurements obtained through global biochemical profiling and targeted assay analysis are plotted. (B) Urine from three donors was serially diluted at least four times and data obtained through global biochemical profiling. AU, arbitrary units.

FIG. 6.

Metabolomics offers the potential to efficiently screen biological matrices such as urine to identify disease and treatment signatures and to refine further analyses to those tests necessary for making definitive diagnoses. (A) The traditional approach starts with a range of potential conditions. To arrive at a diagnosis, many different sample types and tests may be required. (B) The metabolomic method—as demonstrated in this work—offers the potential to screen a single sample type with the metabolomic method to derive the salient disease signature from multiple biochemicals.