Comparison of two metabolomics-platforms to discover biomarkers in critically ill patients from serum analysis
Year: 2024
Type: article
Study design: Observational Study
Abstract: Serum metabolome analysis is essential for identifying disease biomarkers and predicting patient outcomes in precision medicine. Thus, this study aims to compare Ultra-High Performance Liquid Chromatography-High-Resolution Mass Spectrometry (UHPLC-HRMS) with Fourier Transform Infrared (FTIR) spectroscopy in acquiring the serum metabolome of critically ill patients, associated with invasive mechanical ventilation (IMV), and predicting death. Three groups of 8 patients were considered. Group A did not require IMV and survived hospitalization, while Groups B and C required IMV. Group C patients died a median of 5 days after sample harvest. Good prediction models were achieved when comparing groups A to B and B to C using both platforms' data, with UHPLC-HRMS showing 8-17 % higher accuracies (≥83 %). However, developing predictive models using metabolite sets was not feasible when comparing unbalanced populations, i.e., Groups A and B combined to Group C. Alternatively, FTIR-spectroscopy enabled the development of a model with 83 % accuracy. Overall, UHPLC-HRMS data yields more robust prediction models when comparing homogenous populations, potentially enhancing understanding of metabolic mechanisms and improving patient therapy adjustments. FTIR-spectroscopy is more suitable for unbalanced populations. Its simplicity, speed, cost-effectiveness, and high-throughput operation make it ideal for large-scale studies and clinical translation in complex populations.
Institutions: Universidade Politécnica de Lisboa, Comprehensive Health Research Centre, Universidade Nova de Lisboa, University of Lisbon, Instituto Superior Técnico, Centro de Química Estrutural, Unidade Local de Saúde de São José, Institute for Biotechnology and Bioengineering, Institute for Bioengineering and Biosciences
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: hybrid
Estimated APC paid: $2,640
Awards: G1516346500, G1570005492, G261062482, Centro de Química Estrutural, Instituto de Ciências Moleculares, G3432522462, G3455070883, G3845776635, G4961478360, Modelos de Previsão de Desenvolvimento da COVID-19 em Doentes de Risco para uma Medicina de Precisão, G6696461781, Centro de Química Estrutural, G946021960
OpenAlex ID: W4404460965
