Sometimes, there is no playbook. That’s often the case with inborn errors of metabolism (IEM), defined as rare genetic disorders that cause alterations or deficiencies in enzymes involved in metabolism, and other rare diseases. A patient may exhibit clinical signs that don’t map to specific disorders, or a screening panel may come back with no clues. In these frustrating situations, clinical researchers – and patients – need more places to turn for answers.
Applying metabolomics to clinical samples can help screening efforts by vastly expanding the information available to clinical researchers. When used in a clinical setting, metabolomics can measure small molecules in large volumes to identify potential biological pathways that are disturbed in diseased patients. While whole-exome sequencing has provided a valuable systems-based approach in the clinical laboratory, a recently published paper describes a clinical metabolomics approach to drive precision medicine forward, as a method for the screening of metabolic diseases through the analysis of a multi-pronged mass spectrometry platform. In the paper, authored by a team of 11 Metabolon scientists featured in the March 2020 issue of The Journal of Applied Laboratory Medicine, the authors assert that by simultaneously measuring hundreds of metabolites in a single sample, clinical metabolomics offers a comprehensive approach to identify metabolic perturbations across multiple biochemical pathways.
To arrive at their conclusions, the authors performed a single- and multi-day precision study on hundreds of metabolites in human plasma on four, multi-arm, high-throughput metabolomics platforms. This resulted in the reproducibility of the method for the measurement of key IEM metabolites in patient samples across multiple analytical batches, proving the method to be robust and reproducible for the screening of patients with previously undiagnosed inborn errors of metabolism.
Clinical metabolomics has the potential to make precision medicine a reality delivering transformational medicine. Traditional clinical testing of patient samples involves analyzing and measuring individual analytes or biomarkers. In the case of IEM’s, the screening method typically includes three panels, which measure in the range of 50-60 small molecules. Metabolon’s application of clinical metabolomics extends the number of biomarkers that can be measured significantly. Instead of 50-60, we can identify more than 500 small molecules, giving researchers a broader range of biomarkers to mine for insights.
The application of metabolomics is exceptionally significant for IEM and rare disease detection. There are hundreds of IEM diseases, and often the clinical manifestations of those diseases are vague, or the metabolic pathway that is disturbed is not immediately apparent. Applying global, or untargeted, analytical approaches, where prior knowledge of the affected metabolic pathway is not required, provides advantages over targeted analytical approaches and can extend the diagnostic potential of IEM screens beyond the 34-58 disorders that are currently evaluated in newborn screening programs, especially in complex cases. That makes throwing out this wide net to measure as many biomarkers as possible invaluable in a clinical setting.
When evaluating large quantities of information, it is paramount to make data quality a top priority. A critical step in drawing insights from untargeted metabolomics is accurate metabolite identification. Metabolon’s Precision Metabolomics™ workflow delivers Tier 1-2 identifications for detected metabolites and uniquely provides high confidence in the identification of compounds. By contrast, most metabolomic practitioners operate primarily with annotations that only meet the standards of Tiers 3-5, where some unique features are identified. Still, there is low confidence in confirming the metabolite. Data quality is extremely important to Metabolon, with exceptionally high standards applied to our work in clinical metabolomics. From start to finish, we emphasize quality control measures and checks and balances.
Metabolon’s method of clinical metabolomics is validated by Clinical Laboratory Improvement Amendments (CLIA), a regulatory standard for all testing of patient samples. Metabolon is ISO 9001: 2015 certified for analytical and diagnostic testing of biological specimens and accredited by the College of American Pathologists for diagnostic testing on human specimens. Additionally, Metabolon has a New York State Department of Health Clinical Laboratory permit to perform Quantose IR and IGT testing for the identification of insulin resistance and impaired glucose tolerance under the categories of Clinical Chemistry and Endocrinology.
In addition to the validation work we do to demonstrate the properties of the method, such as precision and accuracy, we also run the method under very specific quality control guidelines, which are even stricter than our research guidelines, including running batch controls for each of the analytic batches. We start by setting up the assays following policies that dictate how to establish a reference population, how you qualify the normalizing matrix, and so on. The typical regulatory requirements for review of the results is exceeded, with an additional analytical and lab director review in addition to our typical sub-level review. All of these steps ensure data quality and accurate insights.
Applying our expertise to clinical metabolomics is just one example of how Metabolon reveals biological insights otherwise unseen through other technologies by leveraging our proprietary discovery platform and one of the world’s richest and most diverse patient data sets. We are unique in our ability to receive samples, process them under a clinically validated metabolomics platform that’s controlled for clinical testing, and provide impactful results. Expanding the capabilities of metabolomics to the clinical setting has enormous potential for researchers and patients alike.
Curious about how metabolomics could enrich your study? Contact us today at firstname.lastname@example.org to learn more.