Comparison of Analytical Methods Of Serum Untargeted Metabolomics

Metabolomics has emerged as a powerful tool in the discovery of new biomarkers for medical diagnosis and prognosis. However, there are numerous challenges, such as the methods used to characterize the system metabolome. In the present work, the comparison of two analytical platforms to acquire the serum metabolome of critically ill patients was conducted. The untargeted serum metabolome analysis by ultraperformance liquid chromatography coupled to tandem mass spectrometry (UPLC-MS/MS) enabled to identify a set of metabolites statistically different between deceased and discharged patients. This set of metabolites also enabled to develop a very good predictive model, based on linear discriminant analysis (LDA) with a sensitivity and specificity of 80% and 100%, respectively. Fourier Transform Infrared (FTIR) spectroscopy was also applied in a high-throughput, simple and rapid mode to analyze the serum metabolome. Despite this technique not enabling the identification of metabolites, it allowed to identify molecular fingerprints associated to each patient group, while leading to a good predictive model, based on principal component analysis-LDA, with a sensitivity and specificity of 100% and 90%, respectively. Therefore, both analytical techniques presented complementary characteristics, that should be further explored for metabolome characterization and application as for biomarkers discovery for medical diagnosis and prognosis.

Automated summary

Metabolomics is a powerful tool in discovering new biomarkers for medical diagnosis and prognosis. In this study, two analytical platforms were compared to analyze the serum metabolome of critically ill patients. Untargeted serum metabolome analysis by UPLC-MS/MS identified metabolites that were statistically different between deceased and discharged patients, leading to a good predictive model with high sensitivity and specificity. FTIR spectroscopy, although unable to identify metabolites, identified molecular fingerprints associated with each patient group and also produced a predictive model with high sensitivity and specificity. These techniques offer complementary characteristics for metabolome characterization and biomarker discovery.