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Clinical Validation of Targeted and Untargeted Metabolomics Testing for Genetic Disorders: A 3 Year Comparative Study
Naif A M Almontashiri1,2, Li Zha1, Kim Young1
1Department of Laboratory Medicine, Boston Children's Hospital, Harvard Medical School, Boston, Massachusetts, USA.
Scientific Reports
|June 12, 2020
Summary
Global untargeted metabolomics (GUM) shows 86% sensitivity for detecting known genetic disorders, outperforming traditional targeted metabolomics (TM). However, GUM has a low diagnostic yield for undiagnosed patients with non-specific symptoms.
Area of Science:
- Clinical diagnostics
- Metabolomics
- Genetic disorders
Background:
- Global untargeted metabolomics (GUM) is emerging in clinical diagnostics for genetic disorders.
- Traditional targeted metabolomics (TM) is established for specific metabolite analysis.
Purpose of the Study:
- Compare the clinical utility of GUM versus TM as a screening tool for patients with established genetic disorders.
- Determine GUM's scope as a discovery tool for patients with undiagnosed genetic disorders.
Main Methods:
- Compared TM and GUM data in 226 patients across two cohorts: 87 with confirmed inborn errors of metabolism (IEM) or genetic syndromes, and 139 undiagnosed patients undergoing genetic evaluation.
- Assessed sensitivity and diagnostic yield for both methods.
Main Results:
- GUM demonstrated 86% sensitivity in detecting 51 diagnostic metabolites in patients with known disorders, compared to TM.
- The diagnostic yield of GUM in undiagnosed patients was low at 0.7%.
- GUM detected most diagnostic compounds for known IEMs but showed low yield for non-specific neurological phenotypes.
Conclusions:
- GUM is a sensitive tool for detecting known genetic disorders, comparable to TM.
- Both GUM and TM have limited diagnostic yield in patients with non-specific neurological phenotypes.
- GUM shows potential for validating variants of unknown significance in undiagnosed patients.
