Abstract
Current infection biomarkers are highly limited since they have low capability to predict infection in the presence of confounding processes such as in non-infectious inflammatory processes, low capability to predict disease outcomes and have limited applications to guide and evaluate therapeutic regimes. Therefore, it is critical to discover and develop new and effective clinical infection biomarkers, especially applicable in patients at risk of developing severe illness and critically ill patients. Ideal biomarkers would effectively help physicians with better patient management, leading to a decrease of severe outcomes, personalize therapies, minimize antibiotics overuse and hospitalization time, and significantly improve patient survival. Metabolomics, by providing a direct insight into the functional metabolic outcome of an organism, presents a highly appealing strategy to discover these biomarkers. The present work reviews the desired main characteristics of infection biomarkers, the main metabolomics strategies to discover these biomarkers and the next steps for developing the area towards effective clinical biomarkers.
Highlights
The diagnosis of an infection is usually based on clinical, laboratorial and imaging data
In a following study, based on an in vivo model conducted on Macaca fasticularis infected with E. coli, Langley et al [54] identified a set of metabolites in the plasma that enabled to discriminate non-infected systemic inflammatory response syndrome (SIRS) from sepsis on the CAP and Sepsis Outcome Diagnostics (CAPSOD) and RoCI cohorts after 24 h of hospitalization with area under the curve (AUC) of 0.821 and 0.786, respectively
Due to the critical need to discover infection biomarkers, especially for patients at risk of developing severe diseases, there are some very interesting studies focusing on: the prediction of infection in early stages, as in patients in intensive care unit (ICU); predicting infection among confounding clinical outcomes such as non-infectious inflammatory processes; to discriminate the causative agent; and to predict disease severity and disease outcome, such mortality. Most of these studies are of small dimension, do not use independent data sets for validation, and are not multicenter
Summary
The diagnosis of an infection is usually based on clinical, laboratorial and imaging data. Multiple other studies performed to assess PCT-guided antibiotics discontinuation and mortality in critically ill patients revealed low-certainty evidence, with a high risk of bias, as showed by the conflicting data in the systematic review and meta-analysis [12,13,14,15,16] Another reason why a biomarker of therapeutic responsiveness is desirable, is to predict antibiotic resistance while minimizing the use of broad-spectrum antibiotic. Regarding the management of patients with infections, it is relevant to define biomarkers that effectively will help physicians to diagnose the infection, to predict the causative agent, the disease severity and progression and to monitor the antimicrobial therapy (Figure 1) These biomarkers should be especially applicable in patients in risk of developing severe diseases, critically ill patients, and those presenting confound symptoms that may result in under and over diagnosis of infection. It should provide a concise and precise description of the experimental results, their interpretation, as well as the experimental conclusions that can be drawn
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