Introduction
Approximately 18 million cases of sepsis occur every year worldwide with a mortality rate reaching almost 30% [
1]. However, it has been reported that detecting sepsis, especially at an early stage, improves patient outcome and reduces the mortality rate [
2]. Therefore, it is critical to identify new diagnostic tools and develop prognostic approaches to improve patient care and decrease the sepsis death rate.
In recent years, several studies have been performed to describe and identify biomarkers that could be used in the diagnosis and management of sepsis [
3]. This previous work has suggested that sepsis could be diagnosed by measuring increased levels of particular proteins in blood such as plasma C-reactive protein, inflammatory cytokines (for example tumor necrosis factor α (TNF-α), interleukin-1 (IL-1) and IL-6), procalcitonin or lipopolysaccharide-binding protein [
4]. It has also been reported that the concentrations of lactate or different plasma amino acids can be elevated during the disease [
3,
5,
6]. However, insufficient sensitivity and specificity of the reported compounds currently impede their usage as standard tools for early diagnosis of sepsis [
3,
7,
8]. Therefore, integrated and multifaceted medical approaches supported by effective diagnostic tools, such as a combination of various biomarkers, may improve the identification and the prognosis for sepsis in intensive care units (ICUs) [
3,
9]. Such an integrated approach, based on merging different data sets, could also create broader and more detailed insight into the nature of the disease than can be achieved using one individual approach.
In this study, we have combined metabolomics and multiplex cytokine/chemokine data to investigate its potential for the diagnosis and prognosis of septic shock. It has previously been demonstrated that nuclear magnetic resonance (NMR) spectroscopy-based metabolomics is a very efficient approach for the discovery of molecular markers of sepsis in animals models [
10-
12] and within humans [
13,
14]. In addition, it has been reported that multiplex analysis of cytokines can be used for biomarker identification and quantification in lipopolysaccharide-stimulated human plasma samples [
15]. However, only a limited number of studies have demonstrated success in using a multiplex cytokine/chemokine profiling approach for the prediction of sepsis in clinical settings [
16-
18]. Moreover, to date integration of metabolomics and inflammatory mediator data to identify correlations between immune features and metabolic phenotypes during infection has only been described in an animal model [
19]. By using
1H NMR spectroscopy and multiplex technology we were able to identify and quantify specific metabolites and inflammatory mediators potentially involved in the septic shock response. Using multivariate statistical analysis we could generate powerful models for diagnosis and prognosis of septic shock. This study presents a promising approach for improving patient care and patient outcome in the ICU and deserves further evaluation in other clinical settings, such as the emergency department.
Discussion
The present study is focused on utilizing a combined NMR-based metabolomics and multiplex cytokine/chemokine profiling approach as a potential prognostic evaluation of septic shock. These techniques have several unique advantages over other diagnostic tools. First, quantitative NMR-based metabolomics and bead-based multiplexing for cytokine/chemokine analysis allows for the quantitative measurement of more than 100 different biomarkers from a sample volume less than 250 μL. The ability to maximize this minimal sample volume is essential, as obtaining large samples from critically ill patients, particularly pediatric patients can be difficult. Second, these techniques detect even very small changes in analyte concentration, allowing for the identification of even subtle variations in the patient’s biopattern. Finally, through the analysis of more than 100 biomarkers, we have been able to identify several patterns that otherwise would not have been observed if a smaller, more finite, screen of previously identified biomarkers had been used.
Through the use of multivariate statistical analysis we identified a specific biopattern associated with an early recognition of septic shock. We detected elevated levels of eight metabolites and five inflammatory mediators. Increased concentrations of isobutyrate, myo-inositol, proline and urea indicate hepatic failure and kidney injury [
33-
36]. 3-hydroxybutyrate, O-acetylcarnitine and 2-hydroxybutyrate are metabolites which correlate to energy demands resulting from rising metabolic requirements and inflammatory responses associated with disease conditions [
36,
37]. An elevated level of phenylalanine is the result of an accelerated rate of protein breakdown, as often caused by infections and inflammatory states [
38].
Additionally, the septic shock patients exhibited a remarkable increase in the levels of IP-10 and HGF. It has already been reported that the concentration of IP-10 was elevated in plasma samples of septic shock patients compared to SIRS patients and that IP-10 might serve as a diagnostic marker [
39]. Moreover, it was found that the HGF level was significantly higher in sepsis patients than in the SIRS groups without infection [
40], which correlates with our results. We also observed higher levels of IL-18, IL-1Ra and IL-2Ra in septic shock patients as compared to ICU control patients. The proinflammatory cytokine IL-18 has already been characterized as an important regulator of the innate and acquired immune responses [
41]. Interestingly, IL-1Ra and IL-2Ra are not proinflammatory molecules
per se, but instead represent the body’s response to severe inflammation. IL-1Ra is a cytokine and is an IL-1 receptor antagonist, which has been demonstrated to block the proinflammatory activities of IL-1α and IL1-β [
42,
43]. In contrast, IL-2Ra represents a soluble form of the IL-2 receptor alpha chain that has been released from cell surfaces through the extracellular proteolysis of the IL-2 receptor and functions to bind and block IL-2 resulting in diminished IL-2 signaling [
44]. Although these molecules are not proinflammatory themselves, they have been associated with a number of inflammatory diseases and sepsis [
45,
46] and as such have been proposed to be markers of inflammation.
Furthermore, the concentration of seven metabolites and three inflammatory mediators significantly decreased in septic shock samples compared to the ICU controls. Low levels of glucose and propylene glycol probably result from the rapid oxidation of these metabolites into pyruvate and reflect increasing energy demands during septic shock. The decreased concentration of other compounds (threonine, valine, arginine, glutamate, methanol) is primarily associated with organ dysfunction and/or higher utilization of these metabolites in the disease conditions [
47-
50]. The low level of IL-1α seems to be directly related to the increased concentration of IL-1Ra detected in our study. Interestingly, the level of TNF-β which dropped in septic shock samples was also decreased in septic shock nonsurvivors compared to the survivors in the mortality model. Therefore, a high concentration of TNF-β in serum sample might indicate lower morbidity and better outcome for the ICU patient. The meaning of the decreased level of MCP-3 in septic shock patients could not be explained as it is not well understood how this chemokine is implicated in septic shock. Clearly, further studies of MCP-3 are needed to confirm its importance.
Nonetheless, many of the metabolites and cytokines/chemokines we have observed to be statistically different between septic shock patients and ICU control patients have been previously identified as molecules of interest in sepsis [
3]. Furthermore, many of these molecules have been tested as possible point-of-care diagnostic markers in sepsis but none of the identified markers alone have been adapted into a successful diagnostic test for sepsis [
3]. This failure is likely the result of the multifaceted nature of sepsis; a marker that demonstrates significant association with one group of septic patients may not correlate with all septic patients. As a result, the use of single biomarkers in diagnosis of sepsis has not, and likely will not, result in the development of successful point-of-care testing.
It has previously been demonstrated that applying metabolomics or multiple cytokine assays separately allows for an identification of specific markers associated with sepsis severity. However, to date only metabolomics studies of sepsis have described potentially predictive values. The multiplex inflammatory mediators studies did not propose any predictive model that might be used for early diagnosis of septic shock [
16]. Recently, another study has assessed a multiple cytokine profiling approach to distinguish SIRS and various forms of sepsis within a group of emergency department patients [
17]. Indeed, in this study the authors were able to describe individual mediators independently associated with septic shock. However, the global statistical analysis could not identify any significant differences between the patient groups. In light of these previous reports, our integrated metabolite and cytokine/chemokine study can represent a potentially promising methodology for the prediction of septic shock. The combination of biomarkers such as metabolites and inflammatory mediators yields better results and predictive values than studies previously published and models constructed based on separate datasets only (Table
2).
Additionally, we were able to construct a model for mortality prediction which represents a much better prognostic ability than the commonly used APACHE II and SOFA scores. It should be noted that the application of multiple cytokine assays to predict septic shock outcome has already been described [
18]. However, these authors could only observe a significant mortality odds ratio when using the cytokine/chemokine data collected more than 24 hours after patient enrollment. In contrast, our results are based on blood samples obtained at an earlier stage of patient admission to the ICU (not more than 24 hours). It is well known that the first hours following patient diagnosis are the most important for patient survival and prognosis of patient outcome at this time is very crucial. A similar approach has been described in a recent study in which the authors attempted to integrate metabolomics, proteomics and clinical variables to predict the survival of adult sepsis patients [
51]. Although they performed a broad proteomics analysis by mass spectrometry, they concluded that these results were at best semi-quantitative and they could not incorporate them in their predictive model. Moreover, they also mentioned that their proteome analysis was not sensitive enough to reliably measure cytokines/chemokines in their samples. Since cytokines are known to play an important role during sepsis, we have used a targeted and quantitative cytokine/chemokine proteomics multiplex approach in this study. Our data clearly illustrate that it is possible to integrate quantitative metabolic and cytokine/chemokine proteomic data in a bigger biomarker panel. Furthermore, our study describes a mortality model that is only based on integrated bio-fluid components. This approach may be advantageous to avoid a possible bias associated with a subjective diagnosis by critical care staff. Be that as it may, our method can also easily be extended to include quantitative clinical variables and severity scores.
Acknowledgements
We would like to thank Dr. Deane McIntyre and Dr. Rustem Shaykhutdinov for technical support and for spectrometer maintenance.
The study was supported by the Alberta Sepsis Network, which in turn is funded by Alberta Innovates Health Solutions (AIHS). HJV and PK hold Scientist awards from AIHS, while CNJ is funded by a fellowship from AIHS. CCEPTR receives funding from the Alberta Sepsis Network and from the Canada Foundation for Innovation. PK is also the holder of the Calvin, Phoebe and Joan Snyder Chair in Critical Care Medicine which contributed to this research.
The funding agencies had no influence on the design, analysis and manuscript preparation for this study.
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Competing interests
The authors declare that they have no competing interests.
Authors’ contributions
BM was involved in the conception and design of the study, carrying out NMR experiments, collection and assembly of data, analysis and interpretation of the data, drafting the manuscript, statistical expertise and critical revision of the article for important intellectual content. PT carried out cytokine/chemokine profiling experiments and provided the data for statistical analysis. CNJ participated in cytokine/chemokine profiling experiments, data interpretation and drafting the manuscript. CL coordinated cytokine/chemokine profiling experiments and helped to draft the manuscript. JW supplied blood samples for NMR and cytokine/chemokine profiling experiments and helped to draft the manuscript. BWW coordinated blood samples collection and helped to draft the manuscript. CD participated in the design of the study, its coordination and samples supply. PK was involved in conception and design of the study, critical revision of the manuscript for important intellectual content and final approval of the article. HJV was involved in conception and design of the study, critical revision of the manuscript for important intellectual content and final approval of the manuscript. All authors read and approved the final manuscript.