Abstract
Metabolomics methods often encounter trade-offs between quantification accuracy and coverage, with truly comprehensive coverage only attainable through a multitude of complementary assays. Due to the lack of standardization and the variety of metabolomics assays, it is difficult to integrate datasets across studies or assays. To inform metabolomics platform selection, with a focus on posttraumatic stress disorder (PTSD), we review platform use and sample sizes in psychiatric metabolomics studies and then evaluate five prominent metabolomics platforms for coverage and performance, including intra-/inter-assay precision, accuracy, and linearity. We found performance was variable between metabolite classes, but comparable across targeted and untargeted approaches. Within all platforms, precision and accuracy were highly variable across classes, ranging from 0.9–63.2% (coefficient of variation) and 0.6–99.1% for accuracy to reference plasma. Several classes had high inter-assay variance, potentially impeding dissociation of a biological signal, including glycerophospholipids, organooxygen compounds, and fatty acids. Coverage was platform-specific and ranged from 16–70% of PTSD-associated metabolites. Non-overlapping coverage is challenging; however, benefits of applying multiple metabolomics technologies must be weighed against cost, biospecimen availability, platform-specific normative levels, and challenges in merging datasets. Our findings and open-access cross-platform dataset can inform platform selection and dataset integration based on platform-specific coverage breadth/overlap and metabolite-specific performance.
Highlights
This can be especially impactful for psychopathologies such as posttraumatic stress disorder (PTSD), which relies on subjective symptom reporting for diagnosis and has no biological markers approved by the U.S Food and Drug Administration (FDA)
We found that evidence of metabolomic differences in published PTSD studies is accruing, but remains underexplored compared with other psychiatric conditions (Figure 1A,B,D)
Our findings emphasize that comprehensive coverage is not yet possible for the metabolome and large-scale metabolomics platforms yield distinct coverage, with classspecific performance and measurement variability within and across all platforms
Summary
Metabolomics can characterize the global biochemical activity of a biological entity as it is shaped by external factors including lifestyle and drug [1,2]. Metabolomics can provide valuable insights into the discovery of biological markers of disease states and advance precision medicine efforts through integration with other ‘omics [6,7]. This can be especially impactful for psychopathologies such as PTSD, which relies on subjective symptom reporting for diagnosis and has no biological markers approved by the U.S Food and Drug Administration (FDA). We took an empirical approach to identify the best fit-for-purpose metabolomics platform for characterizing pathological mechanisms or biological disease markers, with a focus on PTSD
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