Izquierdo-Garcia et al. [109] conducted a study in rats where lung tissue, lung lavage fluids and serum samples were profiled from caecal ligation and punctureinduced sepsis and control groups using NMR and highresolution magic angle spinning detection. Predictive PLS-DA models were constructed based on the NMR spectra of the three types of biological samples and employed to diagnose sepsis in other samples. The authors further reported that the predictive power obtained by combining the three types of samples was 100%. An advantage of employing metabolomics for potential diagnosis is the utilisation of biofluids and/or easily accessible tissues. Because lung tissue collection is invasive, while sepsis often appears in critically ill patients, the use of lung tissue for metabolomic analysis is not practical at the bedside. Moreover, bacteria play a key role in sepsis, particularly in conditions like peritonitis-associated sepsis. However, bacterium-related information was absent from their study. Therefore, the diagnostic value of the clinical treatment provided by this study is limited. Future studies may need to include urine samples for metabolomic analysis in order to understand bacterium-specific metabolites.