Prognosis Biomarkers of Severe Sepsis and Septic Shock by 1H NMR Urine Metabolomics in the Intensive Care Unit

Advances in metabolomics in critically ill patients with sepsis and septic shock

Sepsis is associated with high morbidity and mortality rates in hospitalized patients. This condition has a complex pathophysiology and can swiftly progress to the severe form of septic shock, which can lead to organ dysfunction, organ failure, and death. Metabolomics has transformed the clinical and research topography of sepsis, with application to prognosis, diagnosis, and risk assessment. Metabolomics involves detecting and analyzing levels of metabolites in blood (plasma, serum, and/or erythrocytes) and urine; when applied in sepsis, this technology can improve our understanding of the pathogenesis of the disease and aid in better disease management by identifying early biomarkers. For this review article, "metabolomics," "sepsis," and "septic shock" were keywords used to search records in various databases including PubMed and Scopus from their inception until December 2023. This review article summarizes information regarding metabolic profiling performed in sepsis and septic shock and illustrates how metabolomics is advancing the diagnosis and prognosis of patients with sepsis.

Unveiling Pathophysiological Insights: Serum Metabolic Dysregulation in Acute Respiratory Distress Syndrome Patients with Acute Kidney Injury

Acute respiratory distress syndrome (ARDS) is associated with high mortality rates, which are further exacerbated when accompanied by acute kidney injury (AKI). Presently, there is a lack of comprehensive studies thoroughly elucidating the metabolic dysregulation in ARDS patients with AKI leading to poor outcomes. We hypothesized that metabolomics can be a potent tool to highlight the differences in the metabolic profile unraveling unidentified pathophysiological mechanisms of ARDS patients with and without AKI. 1H nuclear magnetic resonance spectroscopy was used to identify key metabolites in the serum samples of 75 patients. Distinct clusters of both groups were obtained as the study's primary outcome using multivariate analysis. Notable alternations in the levels of nine metabolites were identified. Pathway analysis revealed the dysregulation of five significant cycles, which resulted in various complications, such as hyperammonemia, higher energy requirements, and mitochondrial dysfunction causing oxidative stress. Identified metabolites also showed a significant correlation with clinical scores, indicating severity. This study shows the alterations in the metabolite concentration highlighting the difference in the pathophysiology of both patient groups and its association with outcome, pointing in the direction of a personalized medicine approach and holding significant promise for application in critical care settings to improve clinical outcomes.

2,5-Dihydroxyacetophenone attenuates acute kidney injury induced by intra-abdominal infection in rats

AIMS

As one of the most serious complications of sepsis, acute kidney injury (AKI) is pathologically associated with excessive inflammation. 2,5-Dihydroxyacetophenone (DHAP) is isolated from Radix rehmanniae praeparata and exhibit potent anti-inflammatory property. This research aimed at determining the role of DHAP in sepsis-associated AKI (SA-AKI) and the underlying mechanism.

METHODS

Plasma creatinine (Cre), blood urea nitrogen (BUN), tumour necrosis factor-α (TNF-α) and interleukin-1β (IL-1β) levels of SA-AKI patients were detected to evaluate their clinical characteristics. SA-AKI rat models were established by using caecum ligation puncture (CLP) surgery. CLP-induced rats were administered via oral gavage with 20 or 40 mg DHAP after 2 h of CLP surgery. Subsequently, survival rates, serum indexes, histopathological changes, inflammatory factors, renal function indexes and extracellular regulated protein kinases (ERK) and nuclear factor-κB (NF-κB) signalling pathways were detected.

RESULTS

SA-AKI patients exhibited markedly higher levels of plasma Cre, BUN, TNF-α and IL-1β than healthy people. Compared with sham rats, CLP-induced septic rats showed significantly decreased survival rate, increased serum lactate dehydrogenase activity and serum lactate level, obvious renal histopathological injury, upregulated TNF-α, IL-1β and TGF-β1 levels, elevated serum creatinine, BUN and serum cystatin C concentrations, serum neutrophil gelatinase-associated lipocalin and kidney injury molecule-1 levels and reduced renal artery blood flow. All the above CLP-induced changes in septic rats were mitigated after DHAP administration. Additionally, CLP-induced elevation in phosphorylated-ERK1/2 and nuclear NF-κB p65 protein levels was inhibited by DHAP treatment.

CONCLUSION

DHAP hinders SA-AKI progression in rat models by inhibiting ERK and NF-κB signalling pathways.

Sepsis, Management & Advances in Metabolomics

Though there have been developments in clinical care and management, early and accurate diagnosis and risk stratification are still bottlenecks in septic shock patients. Since septic shock is multifactorial with patient-specific underlying co-morbid conditions, early assessment of sepsis becomes challenging due to variable symptoms and clinical manifestations. Moreover, the treatment strategies are traditionally based on their progression and corresponding clinical symptoms, not personalized. The complex pathophysiology assures that a single biomarker cannot identify, stratify, and describe patients affected by septic shock. Traditional biomarkers like CRP, PCT, and cytokines are not sensitive and specific enough to be used entirely for a patient's diagnosis and prognosis. Thus, the need of the hour is a sensitive and specific biomarker after comprehensive analysis that may facilitate an early diagnosis, prognosis, and drug development. Integration of clinical data with metabolomics would provide means to understand the patient's condition, stratify patients better, and predict the clinical outcome.

Metabolic fingerprint of patients showing responsiveness to treatment of septic shock in intensive care unit

OBJECTIVE

An early metabolic signature associated with the responsiveness to treatment can be useful in the better management of septic shock patients. This would help clinicians in designing personalized treatment protocols for patients showing non-responsiveness to treatment.

METHODS

We analyzed the serum on Day 1 (n = 60), Day 3 (n = 47), and Day 5 (n = 26) of patients with septic shock under treatment using NMR-based metabolomics. Partial least square discriminant analysis (PLS-DA) was performed to generate the list of metabolites that can be identified as potential disease biomarkers having statistical significance (that is, metabolites that had a VIP score > 1, and p value < 0.05, False discovery rate (FDR) < 0.05).

RESULTS

Common significant metabolites amongst the three time points were obtained that distinguished the patients being responsive (R) and non-responsive (NR) to treatments, namely 3 hydroxybutyrate, lactate, and phenylalanine which were lower, whereas glutamate and choline higher in patients showing responsiveness.

DISCUSSION

The study gave these metabolic signatures identifying patients' responsiveness to treatment. The results of the study will aid in the development of targeted therapy for ICU patients.

Serum Metabolomic Profiles in Critically Ill Patients with Shock on Admission to the Intensive Care Unit

Inflammatory processes are common in intensive care (ICU) patients and can induce multiple changes in metabolism, leading to increased risks of morbidity and mortality. Metabolomics enables these modifications to be studied and identifies a patient’s metabolic profile. The objective is to precise if the use of metabolomics at ICU admission can help in prognostication. This is a prospective ex-vivo study, realized in a university laboratory and a medico-surgical ICU. Metabolic profiles were analyzed by proton nuclear magnetic resonance. Using multivariable analysis, we compared metabolic profiles of volunteers and ICU patients divided into predefined subgroups: sepsis, septic shock, other shock and ICU controls. We also assessed possible correlations between metabolites and mortality. One hundred and eleven patients were included within 24 h of ICU admission, and 19 healthy volunteers. The ICU mortality rate was 15%. Metabolic profiles were different in ICU patients compared to healthy volunteers (p < 0.001). Among the ICU patients, only the subgroup of patients with septic shock had significant differences compared to the ICU control patients in several metabolites: pyruvate, lactate, carnitine, phenylalanine, urea, creatine, creatinine and myo-inositol. However, there was no correlation between these metabolite profiles and mortality. On the first day of ICU admission, we observed changes in some metabolic products in patients with septic shock, suggesting increased anaerobic glycolysis, proteolysis, lipolysis and gluconeogenesis. These changes were not correlated with prognosis.

Dichloroacetate improves systemic energy balance and feeding behavior during sepsis

Sepsis is a life-threatening organ dysfunction caused by dysregulated host response to an infection. The metabolic aberrations associated with sepsis underly an acute and organism-wide hyperinflammatory response and multiple organ dysfunction; however, crosstalk between systemic metabolomic alterations and metabolic reprogramming at organ levels remains unknown. We analyzed substrate utilization by the respiratory exchange ratio, energy expenditure, metabolomic screening, and transcriptional profiling in a cecal ligation and puncture model to show that sepsis increases circulating free fatty acids and acylcarnitines but decreases levels of amino acids and carbohydrates, leading to a drastic shift in systemic fuel preference. Comparative analysis of previously published metabolomics from septic liver indicated a positive correlation with hepatic and plasma metabolites during sepsis. In particular, glycine deficiency was a common abnormality of the plasma and liver during sepsis. Interrogation of the hepatic transcriptome in septic mice suggested that the septic liver may contribute to systemic glycine deficiency by downregulating genes involved in glycine synthesis. Interestingly, intraperitoneal injection of the pyruvate dehydrogenase kinase (PDK) inhibitor dichloroacetate reversed sepsis-induced anorexia, energy imbalance, inflammation, dyslipidemia, hypoglycemia, and glycine deficiency. Collectively, our data indicated that PDK inhibition rescued systemic energy imbalance and metabolic dysfunction in sepsis partly through restoration of hepatic fuel metabolism.

[Metabolomic changes of neonatal sepsis: an exploratory clinical study]

OBJECTIVES

To study the metabolic mechanism of neonatal sepsis at different stages by analyzing the metabolic pathways involving the serum metabolites with significant differences in neonates with sepsis at different time points after admission.

METHODS

A total of 20 neonates with sepsis who were hospitalized in the Department of Neonatology, Hunan Provincial People's Hospital, from January 1, 2019 to January 1, 2020 were enrolled as the sepsis group. Venous blood samples were collected on days 1, 4, and 7 after admission. Ten healthy neonates who underwent physical examination during the same period were enrolled as the control group. Ultra-performance liquid chromatography-quadrupole time-of-flight mass spectrometry was used for the metabonomic analysis of serum samples to investigate the change in metabolomics in neonates with sepsis at different time points.

RESULTS

On day 1 after admission, the differentially expressed serum metabolites between the sepsis and control groups were mainly involved in the biosynthesis of terpenoid skeleton. For the sepsis group, the differentially expressed serum metabolites between days 1 and 4 after admission were mainly involved in pyruvate metabolism, and those between days 4 and 7 after admission were mainly involved in the metabolism of cysteine and methionine. The differentially expressed serum metabolites between days 1 and 7 after admission were mainly involved in ascorbic acid metabolism.

CONCLUSIONS

The metabolic mechanism of serum metabolites varies at different stages in neonates with sepsis and is mainly associated with terpenoid skeleton biosynthesis, pyruvate metabolism, cysteine/methionine metabolism, and ascorbic acid metabolism.

Patient Stratification in Sepsis: Using Metabolomics to Detect Clinical Phenotypes, Sub-Phenotypes and Therapeutic Response

Infections are common and need minimal treatment; however, occasionally, due to inappropriate immune response, they can develop into a life-threatening condition known as sepsis. Sepsis is a global concern with high morbidity and mortality. There has been little advancement in the treatment of sepsis, outside of antibiotics and supportive measures. Some of the difficulty in identifying novel therapies is the heterogeneity of the condition. Metabolic phenotyping has great potential for gaining understanding of this heterogeneity and how the metabolic fingerprints of patients with sepsis differ based on survival, organ dysfunction, disease severity, type of infection, treatment or causative organism. Moreover, metabolomics offers potential for patient stratification as metabolic profiles obtained from analytical platforms can reflect human individuality and phenotypic variation. This article reviews the most relevant metabolomic studies in sepsis and aims to provide an overview of the metabolic derangements in sepsis and how metabolic phenotyping has been used to identify sub-groups of patients with this condition. Finally, we consider the new avenues that metabolomics could open, exploring novel phenotypes and untangling the heterogeneity of sepsis, by looking at advances made in the field with other -omics technologies.

Transcriptome profiles discriminate between Gram-positive and Gram-negative sepsis in preterm neonates

BACKGROUND

Genome-wide expression profiles have been previously employed as clinical research diagnostic tools for newborn sepsis. We aimed to determine if transcriptomic profiles could discriminate between Gram-positive and Gram-negative bacterial sepsis in preterm infants.

METHODS

Prospective, observational, double-cohort study was conducted in very low birth weight infants with clinical signs and culture-positive sepsis. Blood samples were collected when clinical signs became apparent. Total RNA was processed for transcriptomic analysis. Results were validated by both reverse-transcription polymerase chain reaction and a mathematical model.

RESULTS

We included 25 septic preterm infants, 17 with Gram-positive and 8 with Gram-negative bacteria. The principal component analysis identified these two clusters of patients. We performed a predictive model based on 21 genes that showed an area under the receiver-operating characteristic curve of 1. Eight genes were overexpressed in Gram-positive septic infants: CD37, CSK, MAN2B2, MGAT1, MOB3A, MYO9B, SH2D3C, and TEP1. The most significantly overexpressed pathways were related to metabolic and immunomodulating responses that translated into an equilibrium between pro- and anti-inflammatory responses.

CONCLUSIONS

The transcriptomic profile allowed identification of whether the causative agent was Gram-positive or Gram-negative bacteria. The overexpression of genes such as CD37 and CSK, which control cytokine production and cell survival, could explain the better clinical outcome in sepsis caused by Gram-positive bacteria.

IMPACT

Transcriptomic profiles not only enable an early diagnosis of sepsis in very low birth weight infants but also discriminate between Gram-positive and Gram-negative bacteria as causative agents. The overexpression of some genes related to cytokine production and cell survival could explain the better clinical outcome in sepsis caused by Gram-positive bacteria, and could lead us to a future, targeted therapy.

UPLC-Q-TOF/MS-Based Plasma and Urine Metabolomics Contribute to the Diagnosis of Sepsis

In this study, we aimed to identify potential metabolic biomarkers that can improve the diagnostic accuracy of sepsis. Sixty-six patients including 30 septic and 36 nonsepsis patients from an intensive care unit were recruited. The global plasma and urine metabolomic profiles were determined by ultraperformance liquid chromatography coupled with a quadrupole time-of-flight mass spectrometry-based methodology. The risk factors, including both traditional physiological indicators and metabolic biomarkers, were investigated by binary logistic regression analysis and used to build a least absolute shrinkage and selection operator (Lasso) regression model to evaluate the ability of diagnosis. Fifty-five metabolites in plasma and 11 metabolites in urine were identified through orthogonal projections to latent structures discriminant analysis (OPLS-DA). Among them, ten (PE (20:4(5Z, 8Z, 11Z, 14Z)/P-18:0), harderoporphyrinogen, chloropanaxydiol, (Z)-2-octenal, N¹,N⁸-diacetylspermidine, 1-nitroheptane, venoterpine, α-CEHC, LysoPE (20:0/0:0), corticrocin) metabolites were identified as risk factors. The Lasso regression model incorporating these ten metabolic biomarkers and five traditional physiological indicators displayed better differentiation than the traditional model, represented by the elevated area under receiver operating characteristic curve (AUROC) from 96.80 to 100.0%. Furthermore, patients with septic shock presented a significantly lower level of PE-Cer (d16:1(4E)/19:0). This study suggests that metabolomic profiling could be an effective tool for sepsis diagnosis.

Past Experiences for Future Applications of Metabolomics in Critically Ill Patients with Sepsis and Septic Shocks

A disruption of several metabolic pathways in critically ill patients with sepsis indicates that metabolomics might be used as a more precise tool for sepsis and septic shock when compared with the conventional biomarkers. This article provides information regarding metabolomics studies in sepsis and septic shock patients. It has been shown that a variety of metabolomic pathways are altered in sepsis and septic shock, including amino acid metabolism, fatty acid oxidation, phospholipid metabolism, glycolysis, and tricarboxylic acid cycle. Based upon this comprehensive review, here, we demonstrate that metabolomics is about to change the world of sepsis biomarkers, not only for its utilization in sepsis diagnosis, but also for prognosticating and monitoring the therapeutic response. Additionally, the future direction regarding the establishment of studies integrating metabolomics with other molecular modalities and studies identifying the relationships between metabolomic profiles and clinical characteristics to address clinical application are discussed in this article. All of the information from this review indicates the important impact of metabolomics as a tool for diagnosis, monitoring therapeutic response, and prognostic assessment of sepsis and septic shock. These findings also encourage further clinical investigations to warrant its use in routine clinical settings.

Gender-specific association of oxidative stress and immune response in septic shock mortality using NMR-based metabolomics

Background: Sepsis and septic shock are still associated with a high mortality rate. The early-stage prediction of septic shock outcomes would be helpful to clinicians for designing their treatment protocol. In addition, it would aid clinicians in patient management by understanding gender disparity in terms of clinical outcomes of septic shock by identifying whether there are sex-based differences in sepsis-associated mortality. Objective: This study aimed to test the hypothesis that gender-based metabolic heterogeneity is associated with sepsis survival and identify the biomarkers of mortality for septic shock in an Indian cohort. Method: The study was performed in an Indian population cohort diagnosed with sepsis/septic shock within 24 hours of admission. The study group was 50 patients admitted to intensive care, comprising 23 females and 27 males. Univariate and multivariate analysis were performed to identify the biomarkers for septic shock mortality and the gender-specific metabolic fingerprint in septic shock-associated mortality. Results: The energy-related metabolites, ketone bodies, choline, and NAG were found to be primarily responsible for differentiating survivors and non-survivors. The gender-based mortality stratification identified a female-specific association of the anti-inflammatory response, innate immune response, and β oxidation, and a male-specific association of the pro-inflammatory response to septic shock. Conclusion: The identified mortality biomarkers may help clinicians estimate the severity of a case, as well as predict the outcome and treatment efficacy. The study underlines that gender is one of the most significant biological factors influencing septic shock metabolomic profiles. This understanding can be utilized to identify novel gender-specific biomarkers and innovative targets relevant for gender medicine.

An analysis of urine and serum amino acids in critically ill patients upon admission by means of targeted LC-MS/MS: a preliminary study

Sepsis, defined as a dysregulated host response to infection, causes the interruption of homeostasis resulting in metabolic changes. An examination of patient metabolites, such as amino acids, during the early stage of sepsis may facilitate diagnosing and assessing the severity of the sepsis. The aim of this study was to compare patterns of urine and serum amino acids relative to sepsis, septic shock and survival. Urine and serum samples were obtained from healthy volunteers (n = 15) once or patients (n = 15) within 24 h of a diagnosis of sepsis or septic shock. Concentrations of 25 amino acids were measured in urine and serum samples with liquid chromatography-electrospray mass spectrometry. On admission in the whole cohort, AAA, ABA, mHis, APA, Gly-Pro and tPro concentrations were significantly lower in the serum than in the urine and Arg, Gly, His, hPro, Leu, Ile, Lys, Orn, Phe, Sarc, Thr, Tyr, Asn and Gln were significantly higher in the serum than in the urine. The urine Gly-Pro concentration was significantly higher in septic shock than in sepsis. The serum Cit concentration was significantly lower in septic shock than in sepsis. The urine ABA, mHis and Gly-Pro, and serum Arg, hPro and Orn concentrations were over two-fold higher in the septic group compared to the control group. Urine and serum amino acids measured in septic patients on admission to the ICU may shed light on a patient’s metabolic condition during sepsis or septic shock.

A critical appraisal of the prognostic predictive models for patients with sepsis: Which model can be applied in clinical practice?

BACKGROUND

Sepsis is associated with high mortality and predictive models can help in clinical decision-making. The objective of this study was to carry out a systematic review of these models.

METHODS

In 2019, we conducted a systematic review in MEDLINE and EMBASE (CDR42018111121:PROSPERO) of articles that developed predictive models for mortality in septic patients (inclusion criteria). We followed the CHARMS recommendations (Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies), extracting the information from its 11 domains (Source of data, Participants, etc). We determined the risk of bias and applicability (participants, outcome, predictors and analysis) through PROBAST (Prediction model Risk Of Bias ASsessment Tool).

RESULTS

A total of 14 studies were included. In the CHARMS extraction, the models found showed great variability in its 11 domains. Regarding the PROBAST checklist, only one article had an unclear risk of bias as it did not indicate how missing data were handled while the others all had a high risk of bias. This was mainly due to the statistical analysis (inadequate sample size, handling of continuous predictors, missing data and selection of predictors), since 13 studies had a high risk of bias. Applicability was satisfactory in six articles. Most of the models integrate predictors from routine clinical practice. Discrimination and calibration were assessed for almost all the models, with the area under the ROC curve ranging from 0.59 to 0.955 and no lack of calibration. Only three models were externally validated and their maximum discrimination values in the derivation were from 0.712 and 0.84. One of them (Osborn) had undergone multiple validation studies.

DISCUSSION

Despite most of the studies showing a high risk of bias, we very cautiously recommend applying the Osborn model, as this has been externally validated various times.

Untargeted and Targeted Metabolomic Profiling of Preterm Newborns with EarlyOnset Sepsis: A Case-Control Study

Sepsis is a major concern in neonatology, but there are no reliable biomarkers for its early diagnosis. The aim of the study was to compare the metabolic profiles of plasma and urine samples collected at birth from preterm neonates with and without earlyonset sepsis (EOS) to identify metabolic perturbations that might orient the search for new early biomarkers. All preterm newborns admitted to the neonatal intensive care unit were eligible for this proof-of-concept, prospective case-control study. Infants were enrolled as “cases” if they developed EOS, and as “controls”if they did not. Plasma samples collected at birth and urine samples collected within 24 h of birth underwent untargeted and targeted metabolomic analysis using mass spectrometry coupled with ultra-performance liquid chromatography. Univariate and multivariate statistical analyses were applied. Of 123 eligible newborns, 15 developed EOS. These 15 newborns matched controls for gestational age and weight. Metabolomic analysis revealed evident clustering of the cases versus controls, with the glutathione and tryptophan metabolic pathways markedly disrupted in the former. In conclusion, neonates with EOS had a metabolic profile at birth that clearly distinguished them from those without sepsis, and metabolites of glutathione and tryptophan pathways are promising as new biomarkers of neonatal sepsis.

The 4-Hour Cairns Sepsis Model: A novel approach to predicting sepsis mortality at intensive care unit admission

BACKGROUND

Sepsis commonly causes intensive care unit (ICU) mortality, yet early identification of adults with sepsis at risk of dying in the ICU remains a challenge.

OBJECTIVE

The aim of the study was to derive a mortality prediction model (MPM) to assist ICU clinicians and researchers as a clinical decision support tool for adults with sepsis within 4 h of ICU admission.

METHODS

A cohort study was performed using 500 consecutive admissions between 2014 and 2018 to an Australian tertiary ICU, who were aged ≥18 years and had sepsis. A total of 106 independent variables were assessed against ICU episode-of-care mortality. Multivariable backward stepwise logistic regression derived an MPM, which was assessed on discrimination, calibration, fit, sensitivity, specificity, and predictive values and bootstrapped.

RESULTS

The average cohort age was 58 years, the Acute Physiology and Chronic Health Evaluation III-j severity score was 72, and the case fatality rate was 12%. The 4-Hour Cairns Sepsis Model (CSM-4) consists of age, history of renal disease, number of vasopressors, Glasgow Coma Scale, lactate, bicarbonate, aspartate aminotransferase, lactate dehydrogenase, albumin, and magnesium with an area under the receiver operating characteristic curve of 0.90 (95% confidence interval = 0.84-0.95, p < 0.00001), a Nagelkerke R² of 0.51, specificity of 0.94, a negative predictive value of 0.98, and almost identical odds ratios during bootstrapping. The CSM-4 outperformed existing MPMs tested on our data set. The CSM-4 also performed similar to existing MPMs in their derivation papers whilst using fewer, routinely collected, and inexpensive variables.

CONCLUSIONS

The CSM-4 is a newly derived MPM for adults with sepsis at ICU admission. It displays excellent discrimination, calibration, fit, specificity, negative predictive value, and bootstrapping values whilst being easy to use and inexpensive. External validation is required.

Metabolomics: An emerging potential approach to decipher critical illnesses

Critical illnesses contribute to the maximum morbidity and mortality of hospitalized patients. Acute respiratory distress syndrome (ARDS) and sepsis/septic shock are the two most common acute illnesses associated with intensive care unit (ICU) admission. Once triggered, both have an identical underlying mechanism, portrayed by inflammation and endothelial dysfunction. The diagnosis of ARDS is based on clinical findings, laboratory tests, and radiological imaging. Blood cultures remain the gold standard for the diagnosis of sepsis, with the limitation of time delay and low positive yield. A combination of biomarkers has been proposed to diagnose and prognosticate these acute disorders with strengths and limitations, but still, the gold standard has been elusive to clinicians. In this review article, we illustrate the potential of metabolomics to unravel biomarkers that can be clinically utilized as a rapid prognostic and diagnostic tool associated with specific patient populations (ARDS and sepsis/septic shock) based on the available scientific data.

Lipid metabolic signatures deviate in sepsis survivors compared to non-survivors

Sepsis remains a major cause of death despite advances in medical care. Metabolic deregulation is an important component of the survival process. Metabolomic analysis allows profiling of critical metabolic functions with the potential to classify patient outcome. Our prospective longitudinal characterization of 33 septic and non-septic critically ill patients showed that deviations, independent of direction, in plasma levels of lipid metabolites were associated with sepsis mortality. We identified a coupling of metabolic signatures between liver and plasma of a rat sepsis model that allowed us to apply a human kinetic model of mitochondrial beta-oxidation to reveal differing enzyme concentrations for medium/short-chain hydroxyacyl-CoA dehydrogenase (elevated in survivors) and crotonase (elevated in non-survivors). These data suggest a need to monitor cellular energy metabolism beyond the available biomarkers. A loss of metabolic adaptation appears to be reflected by an inability to maintain cellular (fatty acid) metabolism within a "corridor of safety".

Metabolomic analysis to detect urinary molecular changes associated with bipolar depression

Bipolar disorder (BD) is a debilitating mental disorder with complex clinical manifestations and low diagnostic accuracy. Depressive episodes are most common in the course of BD with high comorbidity and suicide rates, which present greater clinical challenges than mania and hypomania episodes. However, there are no objective biomarkers for bipolar depression. The aim of this study was to detect urinary metabolite biomarkers that could be useful for the diagnosis of bipolar depression. Nuclear magnetic resonance spectroscopy was used to profile urine samples of patients with bipolar depression (n = 37) and healthy volunteers (n = 48). Data were analyzed using Orthogonal Partial Least Square Discriminant Analysis and t-test. Differential metabolites were identified (VIP > 1 and p < 0.05), and further analyzed using Metabo Analyst 3.0 to identify associated metabolic pathways. In total, we identified seven metabolites differentially expressed in patients with BD and healthy controls. Compared with healthy group, the levels of betaine, glycerol, hippuric acid, indole sulfate, trimethylamine oxide, and urea in urine samples of BD patients were significantly higher, while the level of inositol was significantly lower. Most of these small molecules are related to lipid metabolism and gut microbiota metabolism. These differential metabolites could provide critical insight into the pathological mechanisms of bipolar depression. The results of this study provide a meaningful reference for similar and further studies in the future.

Sepsis in the era of data-driven medicine: personalizing risks, diagnoses, treatments and prognoses

Sepsis is a series of clinical syndromes caused by the immunological response to infection. The clinical evidence for sepsis could typically attribute to bacterial infection or bacterial endotoxins, but infections due to viruses, fungi or parasites could also lead to sepsis. Regardless of the etiology, rapid clinical deterioration, prolonged stay in intensive care units and high risk for mortality correlate with the incidence of sepsis. Despite its prevalence and morbidity, improvement in sepsis outcomes has remained limited. In this comprehensive review, we summarize the current landscape of risk estimation, diagnosis, treatment and prognosis strategies in the setting of sepsis and discuss future challenges. We argue that the advent of modern technologies such as in-depth molecular profiling, biomedical big data and machine intelligence methods will augment the treatment and prevention of sepsis. The volume, variety, veracity and velocity of heterogeneous data generated as part of healthcare delivery and recent advances in biotechnology-driven therapeutics and companion diagnostics may provide a new wave of approaches to identify the most at-risk sepsis patients and reduce the symptom burden in patients within shorter turnaround times. Developing novel therapies by leveraging modern drug discovery strategies including computational drug repositioning, cell and gene-therapy, clustered regularly interspaced short palindromic repeats -based genetic editing systems, immunotherapy, microbiome restoration, nanomaterial-based therapy and phage therapy may help to develop treatments to target sepsis. We also provide empirical evidence for potential new sepsis targets including FER and STARD3NL. Implementing data-driven methods that use real-time collection and analysis of clinical variables to trace, track and treat sepsis-related adverse outcomes will be key. Understanding the root and route of sepsis and its comorbid conditions that complicate treatment outcomes and lead to organ dysfunction may help to facilitate identification of most at-risk patients and prevent further deterioration. To conclude, leveraging the advances in precision medicine, biomedical data science and translational bioinformatics approaches may help to develop better strategies to diagnose and treat sepsis in the next decade.

Linking Big Data and Prediction Strategies: Tools, Pitfalls, and Lessons Learned

OBJECTIVES

Modern critical care amasses unprecedented amounts of clinical data-so called "big data"-on a minute-by-minute basis. Innovative processing of these data has the potential to revolutionize clinical prognostics and decision support in the care of the critically ill but also forces clinicians to depend on new and complex tools of which they may have limited understanding and over which they have little control. This concise review aims to provide bedside clinicians with ways to think about common methods being used to extract information from clinical big datasets and to judge the quality and utility of that information.

DATA SOURCES

We searched the free-access search engines PubMed and Google Scholar using the MeSH terms "big data", "prediction", and "intensive care" with iterations of a range of additional potentially associated factors, along with published bibliographies, to find papers suggesting illustration of key points in the structuring and analysis of clinical "big data," with special focus on outcomes prediction and major clinical concerns in critical care.

STUDY SELECTION

Three reviewers independently screened preliminary citation lists.

DATA EXTRACTION

Summary data were tabulated for review.

DATA SYNTHESIS

To date, most relevant big data research has focused on development of and attempts to validate patient outcome scoring systems and has yet to fully make use of the potential for automation and novel uses of continuous data streams such as those available from clinical care monitoring devices.

CONCLUSIONS

Realizing the potential for big data to improve critical care patient outcomes will require unprecedented team building across disparate competencies. It will also require clinicians to develop statistical awareness and thinking as yet another critical judgment skill they bring to their patients' bedsides and to the array of evidence presented to them about their patients over the course of care.

Nutritional support in sepsis: when less may be more

Despite sound basis to suspect that aggressive and early administration of nutritional support may hold therapeutic benefits during sepsis, recommendations for nutritional support have been somewhat underwhelming. Current guidelines (ESPEN and ASPEN) recognise a lack of clear evidence demonstrating the beneficial effect of nutritional support during sepsis, raising the question: why, given the perceived low efficacy of nutritionals support, are there no high-quality clinical trials on the efficacy of permissive underfeeding in sepsis? Here, we review clinically relevant beneficial effects of permissive underfeeding, motivating the urgent need to investigate the clinical benefits of delaying nutritional support during sepsis.

Sepsis: Precision-Based Medicine for Pregnancy and the Puerperium

Sepsis contributes significantly to global morbidity and mortality, particularly in vulnerable populations. Pregnant and recently pregnant women are particularly prone to rapid progression to sepsis and septic shock, with 11% of maternal deaths worldwide being attributed to sepsis. The impact on the neonate is considerable, with 1 million neonatal deaths annually attributed to maternal infection or sepsis. Pregnancy specific physiological and immunological adaptations are likely to contribute to a greater impact of infection, but current approaches to the management of sepsis are based on those developed for the non-pregnant population. Pregnancy-specific strategies are required to optimise recognition and management of these patients. We review current knowledge of the physiology and immunology of pregnancy and propose areas of research, which may advance the development of pregnancy-specific diagnostic and therapeutic approaches to optimise the care of pregnant women and their babies.

Identification of Metabolic Changes in Ileum, Jejunum, Skeletal Muscle, Liver, and Lung in a Continuous I.V. Pseudomonas aeruginosa Model of Sepsis Using Nontargeted Metabolomics Analysis

Sepsis is a multiorgan disease affecting the ileum and jejunum (small intestine), liver, skeletal muscle, and lung clinically. The specific metabolic changes in the ileum, jejunum, liver, skeletal muscle, and lung have not previously been investigated. Live Pseudomonas aeruginosa, isolated from a patient, was given via i.v. catheter to pigs to induce severe sepsis. Eighteen hours later, ileum, jejunum, medial gastrocnemius skeletal muscle, liver, and lung were analyzed by nontargeted metabolomics analysis using gas chromatography/mass spectrometry. The ileum and the liver demonstrated significant changes in metabolites involved in linoleic acid metabolism: the ileum and lung had significant changes in the metabolism of valine/leucine/isoleucine; the jejunum, skeletal muscle, and liver had significant changes in arginine/proline metabolism; and the skeletal muscle and lung had significant changes in aminoacyl-tRNA biosynthesis, as analyzed by pathway analysis. Pathway analysis also identified changes in metabolic pathways unique for different tissues, including changes in the citric acid cycle (jejunum), β-alanine metabolism (skeletal muscle), and purine metabolism (liver). These findings demonstrate both overlapping metabolic pathways affected in different tissues and those that are unique to others and provide insight into the metabolic changes in sepsis leading to organ dysfunction. This may allow therapeutic interventions that focus on multiple tissues or single tissues once the relationship of the altered metabolites/metabolism to the underlying pathogenesis of sepsis is determined.

A prospective pilot study using metabolomics discloses specific fatty acid, catecholamine and tryptophan metabolic pathways as possible predictors for a negative outcome after severe trauma

Background

We wanted to define metabolomic patterns in plasma to predict a negative outcome in severe trauma patients.

Methods

A prospective pilot study was designed to evaluate plasma metabolomic patterns, established by liquid chromatography coupled to mass spectrometry, in patients allocated to an intensive care unit (in the University Hospital Arnau de Vilanova, Lleida, Spain) in the first hours after a severe trauma (n = 48). Univariate and multivariate statistics were employed to establish potential predictors of mortality.

Results

Plasma of patients non surviving to trauma (n = 5) exhibited a discriminating metabolomic pattern, involving basically metabolites belonging to fatty acid and catecholamine synthesis as well as tryptophan degradation pathways. Thus, concentration of several metabolites exhibited an area under the receiver operating curve (ROC) higher than 0.84, including 3-indolelactic acid, hydroxyisovaleric acid, phenylethanolamine, cortisol, epinephrine and myristic acid. Multivariate binary regression logistic revealed that patients with higher myristic acid concentrations had a non-survival odds ratio of 2.1 (CI 95% 1.1–3.9).

Conclusions

Specific fatty acids, catecholamine synthesis and tryptophan degradation pathways could be implicated in a negative outcome after trauma. The metabolomic study of severe trauma patients could be helpful for biomarker proposal.

Electronic supplementary material

The online version of this article (10.1186/s13049-019-0631-5) contains supplementary material, which is available to authorized users.

A pilot case-control study of urine metabolomics in preterm neonates with necrotizing enterocolitis

Necrotizing enterocolitis (NEC) is a leading cause of gastrointestinal morbidity and mortality in preterm neonates. The aim of this pilot study was to explore using metabolomics alternations in the urine metabolites related to NEC that could possibly serve as diagnostic biomarkers of the disease. Urine samples were prospectively collected at the day of initial evaluation for NEC from 15 diseased preterm neonates (five Bell's stage I and ten stage II/III) and an equal number of matched controls. Urine metabolic profiles were assessed using non-targeted nuclear magnetic resonance spectroscopy and targeted liquid chromatography-tandem mass spectrometry monitoring 108 metabolites. Multivariate statistical models with data from either analytical approach showed clear separation between the metabolic profiles of neonates with NEC and controls. Twenty-five discriminant metabolites were identified belonging to amino and organic acids, sugars and vitamins. A number of metabolite combinations were found to have an excellent diagnostic performance in detecting neonates developing NEC. Our results show that the metabolic profile of neonates with NEC differs significantly from that of controls, making possible their separation using urine metabolomic analysis. Nevertheless, whether the small set of significant metabolites detected in this investigation could be used as early diagnostic biomarkers of NEC should be validated in larger studies.

Septic Shock Nonsurvivors Have Persistently Elevated Acylcarnitines Following Carnitine Supplementation

INTRODUCTION

Sepsis-induced metabolic disturbances include hyperlactatemia, disruption of glycolysis, protein catabolism, and altered fatty acid metabolism. It may also lower serum L-carnitine that supports the use of L-carnitine supplementation as a treatment to ameliorate several of these metabolic consequences.

METHODS

To further understand the association between L-carnitine-induced changes in serum acylcarnitines, fatty acid metabolism and survival, serum samples from (T0), 12 hfollowing completion (T24) of L-carnitine (n = 16) or placebo (n = 15) administration, and 48 h (T48) after enrollment from patients with septic shock enrolled in a randomized control trial were assayed for acylcarnitines, free fatty acids, and insulin. Data were analyzed comparing 1-year survivors and nonsurvivors within treatment groups.

RESULTS

Mortality was 8 of 16 (50%) and 12 of 15 (80%) at 1 year for L-carnitine and placebo-treated patients, respectively. Free carnitine, C2, C3, and C8 acylcarnitines were higher among nonsurvivors at enrollment. L-Carnitine treatment increased levels of all measured acylcarnitines; an effect that was sustained for at least 36 h following completion of the infusion and was more prominent among nonsurvivors. Several fatty acids followed a similar, though less consistent pattern. Glucose, lactate, and insulin levels did not differ based on survival or treatment arm.

CONCLUSIONS

In human patients with septic shock, L-Carnitine supplementation increases a broad range of acylcarnitine concentrations that persist after cessation of infusion, demonstrating both immediate and sustained effects on the serum metabolome. Nonsurvivors demonstrate a distinct metabolic response to L-carnitine compared with survivors, which may indicate preexisting or more profound metabolic derangement that constrains any beneficial response to treatment.

Metabolomics applied to maternal and perinatal health: a review of new frontiers with a translation potential

The prediction or early diagnosis of maternal complications is challenging mostly because the main conditions, such as preeclampsia, preterm birth, fetal growth restriction, and gestational diabetes mellitus, are complex syndromes with multiple underlying mechanisms related to their occurrence. Limited advances in maternal and perinatal health in recent decades with respect to preventing these disorders have led to new approaches, and "omics" sciences have emerged as a potential field to be explored. Metabolomics is the study of a set of metabolites in a given sample and can represent the metabolic functioning of a cell, tissue or organism. Metabolomics has some advantages over genomics, transcriptomics, and proteomics, as metabolites are the final result of the interactions of genes, RNAs and proteins. Considering the recent "boom" in metabolomic studies and their importance in the research agenda, we here review the topic, explaining the rationale and theory of the metabolomic approach in different areas of maternal and perinatal health research for clinical practitioners. We also demonstrate the main exploratory studies of these maternal complications, commenting on their promising findings. The potential translational application of metabolomic studies, especially for the identification of predictive biomarkers, is supported by the current findings, although they require external validation in larger datasets and with alternative methodologies.

Succinate receptor mediates intestinal inflammation and fibrosis

Succinate, an intermediate of the tricarboxylic acid cycle, is accumulated in inflamed areas and its signaling through succinate receptor (SUCNR1) regulates immune function. We analyze SUCNR1 expression in the intestine of Crohn's disease patients and its role in murine intestinal inflammation and fibrosis. We show that both serum and intestinal succinate levels and SUCNR1 expression in intestinal surgical resections were higher in CD patients than in controls. SUCNR1 co-localized with CD86, CD206, and α-SMA+ cells in human intestine and we found a positive and significant correlation between SUCNR1 and α-SMA expression. In human isolated fibroblasts from CD patients SUCNR1 expression was higher than in those from controls and treatment with succinate increased SUCNR1 expression, fibrotic markers and inflammatory cytokines through SUCNR1. This receptor modulated the expression of pro-inflammatory cytokines in resting murine macrophages, macrophage polarization and fibroblast activation and Sucnr1^-/- mice were protected against both acute TNBS-colitis and intestinal fibrosis induced by the heterotopic transplant of colonic tissue. We demonstrate increased succinate levels in serum and SUCNR1 expression in intestinal tissue of CD patients and show a role for SUCNR1 in murine intestinal inflammation and fibrosis.

Identification of novel metabolomic biomarkers in an experimental model of septic acute kidney injury

The aim of this study is the identification of metabolomic biomarkers of sepsis and sepsis-induced acute kidney injury (AKI) in an experimental model. Pigs were anesthetized and monitored to measure mean arterial pressure (MAP), systemic blood flow (Q_T), mean pulmonary arterial pressure, renal artery blood flow (Q_RA), renal cortical blood flow (Q_RC), and urine output (UO). Sepsis was induced at t = 0 min by the administration of live Escherichia coli ( n = 6) or saline ( n = 8). At t = 300 min, animals were killed. Renal tissue, urine, and serum samples were analyzed by nuclear magnetic resonance (NMR) spectroscopy. Principal component analyses were performed on the processed NMR spectra to highlight kidney injury biomarkers. Sepsis was associated with decreased Q_T and MAP and decreased Q_RA, Q_RC, and UO. Creatinine serum concentration and neutrophil gelatinase-associated lipocalin (NGAL) serum and urine concentrations increased. NMR-based metabolomics analysis found metabolic differences between control and septic animals: 1) in kidney tissue, increased lactate and nicotinuric acid and decreased valine, aspartate, glucose, and threonine; 2) in urine, increased isovaleroglycine, aminoadipic acid, N-acetylglutamine, N-acetylaspartate, and ascorbic acid and decreased myoinositol and phenylacetylglycine; and 3) in serum, increased lactate, alanine, pyruvate, and glutamine and decreased valine, glucose, and betaine concentrations. The concentration of several metabolites altered in renal tissue and urine samples from septic animals showed a significant correlation with markers of AKI (i.e., creatinine and NGAL serum concentrations). NMR-based metabolomics is a potentially useful tool for biomarker identification of sepsis-induced AKI.

The role of bile acids in nutritional support

Nutritional support continues to receive much attention as a possible intervention to prevent loss of lean tissue mass, promote recovery and re-establish proper immune function in critical care patients. Yet there remains much controversy regarding the clinical efficacy of such interventions. In addition to the direct effect of nutrition in terms of micro- and macronutrient content, nutritional formulations may exert an effect via the physiological response to feeding. Here, we highlight the key role of postprandial reabsorbed bile acids in attenuating both the inflammatory response and autophagy. These observations suggest that not all patients would benefit from aggressive nutritional support.

Gingerol suppresses sepsis-induced acute kidney injury by modulating methylsulfonylmethane and dimethylamine production

Acute kidney injury (AKI) and metabolic dysfunction are critical complications in sepsis syndrome; however, their pathophysiological mechanisms remain poorly understood. Therefore, we evaluated whether the pharmacological properties of 6-gingerol (6G) and 10-gingerol (10G) could modulate AKI and metabolic disruption in a rat model of sepsis (faecal peritonitis). Animals from the sham and AKI groups were intraperitoneally injected with 6G or 10G (25 mg/kg). Septic AKI decreased creatinine clearance and renal antioxidant activity, but enhanced oxidative stress and the renal mRNA levels of tumour necrosis factor-α, interleukin-1β, and transforming growth factor-β. Both phenol compounds repaired kidney function through antioxidant activity related to decreased oxidative/nitrosative stress and proinflammatory cytokines. Metabolomics analysis indicated different metabolic profiles for the sham surgery group, caecal ligation and puncture model alone group, and sepsis groups treated with gingerols. ¹H nuclear magnetic resonance analysis detected important increases in urinary creatine, allantoin, and dimethylglycine levels in septic rats. However, dimethylamine and methylsulfonylmethane metabolites were more frequently detected in septic animals treated with 6G or 10G, and were associated with increased survival of septic animals. Gingerols attenuated septic AKI by decreasing renal disturbances, oxidative stress, and inflammatory response through a mechanism possibly correlated with increased production of dimethylamine and methylsulfonylmethane.

Evaluation of a combination "lymphocyte apoptosis model" to predict survival of sepsis patients in an intensive care unit

Background

A major challenge in sepsis intervention is unclear risk stratification. We postulated that a panel of biomarkers of lymphocyte apoptosis and immune function, termed the “lymphocyte apoptosis model,” would be an effective tool for predicting 28-day survival for sepsis patients.

Methods

A total of 52 consecutive sepsis patients were enrolled. Peripheral blood samples were collected on day 1 of admission for quantification of biomarkers of lymphocyte apoptosis and immune function, including lymphocyte count, lymphocyte apoptotic percentage, expression on monocyte HLA-DR, CD4⁺/CD8⁺ T cell ratio, T helper type 1 to type 2 ratio (Th1/Th2), cytochrome c levels, and various proinflammatory cytokine levels. Sepsis severity was classified using Acute Physiology and Chronic Health Evaluation II (APACHE II) and Sequential Organ Failure Assessment (SOFA) scores. Survival was assessed at 28 days.

Results

Compared with survivors, non-survivors had significantly higher lymphocyte apoptotic percentages and plasma cytochrome c levels and significantly lower lymphocyte counts, Th1/Th2 ratios, and HLA-DR expression on day 1 of admission. Multivariate analysis identified cytochrome c levels (odds ratio [OR]1.829, p = 0.025), lymphocyte apoptotic percentage (OR 1.103, p = 0.028), lymphocyte count (OR 0.150, p = 0.047), and HLA-DR expression (OR 0.923, p = 0.021) as independent predictors of 28-day mortality. A logistic regression equation incorporating the independent risk factors predicted 28-day mortality with greater accuracy than did the APACHE II score or single components biomarkers.

Conclusions

The “lymphocyte apoptosis model” may be useful for risk stratification and predicting prognosis of sepsis patients.

Sepsis Diagnostics in the Era of "Omics" Technologies

Sepsis is a multifactorial clinical syndrome with an extremely dynamic clinical course and with high diverse clinical phenotype. Early diagnosis is crucial for the final clinical outcome. Previous studies have not identified a biomarker for the diagnosis of sepsis which would have sufficient sensitivity and specificity. Identification of the infectious agents or the use of molecular biology, next gene sequencing, has not brought significant benefit for the patient in terms of early diagnosis. Therefore, we are currently searching for biomarkers, through "omics" technologies with sufficient diagnostic specificity and sensitivity, able to predict the clinical course of the disease and the patient response to therapy. Current progress in the use of systems biology technologies brings us hope that by using big data from clinical trials such biomarkers will be found.

Novel Biomarker Candidates for Febrile Neutropenia in Hematological Patients Using Nontargeted Metabolomics

Background

Novel potential small molecular biomarkers for sepsis were analyzed with nontargeted metabolite profiling to find biomarkers for febrile neutropenia after intensive chemotherapy for hematological malignancies.

Methods

Altogether, 85 patients were included into this prospective study at the start of febrile neutropenia after intensive chemotherapy for acute myeloid leukemia or after autologous stem cell transplantation. The plasma samples for the nontargeted metabolite profiling analysis by liquid chromatography-mass spectrometry were taken when fever rose over 38° and on the next morning.

Results

Altogether, 90 differential molecular features were shown to explain the differences between patients with complicated (bacteremia, severe sepsis, or fatal outcome) and noncomplicated courses of febrile neutropenia. The most differential compounds were an androgen hormone, citrulline, and phosphatidylethanolamine PE(18:0/20:4). The clinical relevance of the findings was evaluated by comparing them with conventional biomarkers like C-reactive protein and procalcitonin.

Conclusion

These results hold promise to find out novel biomarkers for febrile neutropenia, including citrulline. Furthermore, androgen metabolism merits further studies.

Metabolomics: From liver chiromancy to personalized precision medicine in advanced chronic liver disease

Currently there is a lack of accurate biomarkers for diagnosis and prognosis in advanced liver diseases. Either the occurrence of first decompensation, or diagnosis of acute on chronic liver failure, severe alcoholic hepatitis, or hepatocellular carcinoma (HCC), none of the available biomarkers are satisfactory. Metabolomics is the newest of omics, being much closer than the others to the actual phenotype and pathologic changes that characterizes a certain condition. It deals with a much wider spectrum of low molecular weight bio-compounds providing a powerful platform for discovering novel biomarkers and biochemical pathways to improve diagnostic, prognostication and therapy. Until now metabolomics was applied in a wide spectrum of liver conditions, but the findings were contradictory. This review proposes a synthesis of the existing evidences of metabolomics use in advanced chronic liver diseases, decompensated liver cirrhosis, severe alcoholic hepatitis and HCC.

NMR-Based Plasma Metabolomics at Set Intervals in Newborn Dairy Calves with Severe Sepsis

The aim of this first study was to reveal the new potential biomarkers by a metabolomics approach in severe septic calves. Sepsis is a common cause of morbidity and mortality in newborn dairy calves. The main challenges with the use of biomarkers of sepsis in domestic animals are their availability, cost, and time required to obtain a result. Metabolomics may offer the potential to identify biomarkers that define calf sepsis in terms of combined clinical, physiological, and pathobiological abnormalities. To our knowledge, this is the first study presenting an NMR- (nuclear magnetic resonance-) based plasma metabolomics at set intervals in neonatal septic calves. Twenty neonatal dairy calves with severe sepsis and ten healthy calves were used. Hematological and biochemical health profiles were gathered in plasma samples at set intervals. Similarly, NMR spectra were acquired. All diseased animals (except one) died after 72 hours. Clinical and laboratory results were in accordance with those of severe septic animals. Multivariate analysis on NMR plasma spectra proved to be an excellent tool for faster identification of calves with severe sepsis from healthy animals. The NMR-based metabolomic profile may contribute to the better understanding of severe sepsis in newborn calves.

Metabolomics in Sepsis and Its Impact on Public Health

Sepsis, with its often devastating consequences for patients and their families, remains a major public health concern that poses an increasing financial burden. Early resuscitation together with the elucidation of the biological pathways and pathophysiological mechanisms with the use of "-omics" technologies have started changing the clinical and research landscape in sepsis. Metabolomics (i.e., the study of the metabolome), an "-omics" technology further down in the "-omics" cascade between the genome and the phenome, could be particularly fruitful in sepsis research with the potential to alter the clinical practice. Apart from its benefit for the individual patient, metabolomics has an impact on public health that extends beyond its applications in medicine. In this review, we present recent developments in metabolomics research in sepsis, with a focus on pneumonia, and we discuss the impact of metabolomics on public health, with a focus on free/libre open source software.

The oxylipin and endocannabidome responses in acute phase Plasmodium falciparum malaria in children

BACKGROUND

Oxylipins and endocannabinoids are low molecular weight bioactive lipids that are crucial for initiation and resolution of inflammation during microbial infections. Metabolic complications in malaria are recognized contributors to severe and fatal malaria, but the impact of malaria infection on the production of small lipid derived signalling molecules is unknown. Knowledge of immunoregulatory patterns of these molecules in malaria is of great value for better understanding of the disease and improvement of treatment regimes, since the action of these classes of molecules is directly connected to the inflammatory response of the organism.

METHODS

Detection of oxylipins and endocannabinoids from plasma samples from forty children with uncomplicated and severe malaria as well as twenty controls was done after solid phase extraction followed by chromatography mass spectrometry analysis. The stable isotope dilution method was used for compound quantification. Data analysis was done with multivariate (principal component analysis (PCA), orthogonal partial least squares discriminant analysis (OPLS-DA®) and univariate approaches (receiver operating characteristic (ROC) curves, t tests, correlation analysis).

RESULTS

Forty different oxylipin and thirteen endocannabinoid metabolites were detected in the studied samples, with one oxylipin (thromboxane B2, TXB2) in significantly lower levels and four endocannabinoids (OEA, PEA, DEA and EPEA) at significantly higher levels in infected individuals as compared to controls according to t test analysis with Bonferroni correction. Three oxylipins (13-HODE, 9-HODE and 13-oxo-ODE) were higher in severe compared to uncomplicated malaria cases according to the results from multivariate analysis. Observed changes in oxylipin levels can be connected to activation of cytochrome P450 (CYP) and 5-lipoxygenase (5-LOX) metabolic pathways in malaria infected individuals compared to controls, and related to increased levels of all linoleic acid oxylipins in severe patients compared to uncomplicated ones. The endocannabinoids were extremely responsive to malaria infection with majority of this class of molecules found at higher levels in infected individuals compared to controls.

CONCLUSIONS

It was possible to detect oxylipin and endocannabinoid molecules that can be potential biomarkers for differentiation between malaria infected individuals and controls and between different classes of malaria. Metabolic pathways that could be targeted towards an adjunctive therapy in the treatment of malaria were also pinpointed.

A new mass spectrometry-based method for the quantification of histones in plasma from septic shock patients

The aim of this study was to develop a novel method to detect circulating histones H3 and H2B in plasma based on multiple reaction monitoring targeted mass spectrometry and a multiple reaction monitoring approach (MRM-MS) for its clinical application in critical bacteriaemic septic shock patients. Plasma samples from 17 septic shock patients with confirmed bacteraemia and 10 healthy controls were analysed by an MRM-MS method, which specifically detects presence of histones H3 and H2B. By an internal standard, it was possible to quantify the concentration of circulating histones in plasma, which were significantly higher in patients, and thus confirmed their potential as biomarkers for diagnosing septic shock. After comparing surviving patients and non-survivors, a correlation was found between higher levels of circulating histones and unfavourable outcome. Indeed, histone H3 proved a more efficient and sensitive biomarker for septic shock prognosis. In conclusion, these findings suggest the accuracy of the MRM-MS technique and stable isotope labelled peptides to detect and quantify circulating plasma histones H2B and H3. This method may be used for early septic shock diagnoses and for the prognosis of fatal outcomes.

Metabolomics as a Driver in Advancing Precision Medicine in Sepsis

The objective of this review is to explain the science of metabolomics-a science of systems biology that measures and studies endogenous small molecules (metabolites) that are present in a single biological sample-and its application to the diagnosis and treatment of sepsis. In addition, we discuss how discovery through metabolomics can contribute to the development of precision medicine targets for this complex disease state and the potential avenues for those new discoveries to be applied in the clinical environment. A nonsystematic literature review was performed focusing on metabolomics, pharmacometabolomics, and sepsis. Human (adult and pediatric) and animal studies were included. Metabolomics has been investigated in the diagnosis, prognosis, and risk stratification of sepsis, as well as for the identification of drug target opportunities. Metabolomics elucidates a new level of detail when compared with other systems biology sciences, with regard to the metabolites that are most relevant in the pathophysiology of sepsis, as well as highlighting specific biochemical pathways at work in sepsis. Metabolomics also highlights biochemical differences between sepsis survivors and nonsurvivors at a level of detail greater than that demonstrated by genomics, transcriptomics, or proteomics, potentially leading to actionable targets for new therapies. The application of pharmacometabolomics and its integration with other systems pharmacology to sepsis therapeutics could be particularly helpful in differentiating drug responders and nonresponders and furthering knowledge of mechanisms of drug action and response. The accumulated literature on metabolomics suggests it is a viable tool for continued discovery around the pathophysiology, diagnosis and prognosis, and treatment of sepsis in both adults and children, and it provides a greater level of biochemical detail and insight than other systems biology approaches. However, the clinical application of metabolomics in sepsis has not yet been fully realized. Prospective validation studies are needed to translate metabolites from the discovery phase into the clinical utility phase.

Chemotherapy-induced gastrointestinal toxicity is associated with changes in serum and urine metabolome and fecal microbiota in male Sprague-Dawley rats

Purpose

Chemotherapy-induced gastrointestinal toxicity (CIGT) is a complex process that involves multiple pathophysiological mechanisms. We have previously shown that commonly used chemotherapeutics 5-fluorouracil, oxaliplatin, and irinotecan damage the intestinal mucosa and increase intestinal permeability to iohexol. We hypothesized that CIGT is associated with alterations in fecal microbiota and metabolome. Our aim was to characterize these changes and examine how they relate to the severity of CIGT.

Methods

A total of 48 male Sprague–Dawley rats were injected intraperitoneally either with 5-fluorouracil (150 mg/kg), oxaliplatin (15 mg/kg), or irinotecan (200 mg/kg). Body weight change was measured daily after drug administration and the animals were euthanized after 72 h. Blood, urine, and fecal samples were collected at baseline and at the end of the experiment. The changes in the composition of fecal microbiota were analyzed with 16S rRNA gene sequencing. Metabolic changes in serum and urine metabolome were measured with 1 mm proton nuclear magnetic resonance (¹H-NMR).

Results

Irinotecan increased the relative abundance of Fusobacteria and Proteobacteria, while 5-FU and oxaliplatin caused only minor changes in the composition of fecal microbiota. All chemotherapeutics increased the levels of serum fatty acids and N(CH₃)₃ moieties and decreased the levels of Krebs cycle metabolites and free amino acids.

Conclusions

Chemotherapeutic drugs, 5-fluorouracil, oxaliplatin, and irinotecan, induce several microbial and metabolic changes which may play a role in the pathophysiology of CIGT. The observed changes in intestinal permeability, fecal microbiota, and metabolome suggest the activation of inflammatory processes.

Electronic supplementary material

The online version of this article (doi:10.1007/s00280-017-3364-z) contains supplementary material, which is available to authorized users.

High-throughput and multi-dimensional omics approach uncovers a huaxian capsule to ameliorate the dysregulated expression profiling of severe sepsis rats

Multi-dimensional omics could be helpful to interpret the underlying mechanisms of disease.

High-throughput lipidomics enables discovery of the mode of action of huaxian capsule impacting the metabolism of sepsis

Severe sepsis (SS) is a major cause of mortality and morbidity in the intensive care unit and requires rapid diagnosis and treatment.

Metabolic Disturbances in Adult-Onset Still's Disease Evaluated Using Liquid Chromatography/Mass Spectrometry-Based Metabolomic Analysis

Objective

Liquid chromatography/mass spectrometry (LC/MS)-based comprehensive analysis of metabolic profiles with metabolomics approach has potential diagnostic and predictive implications. However, no metabolomics data have been reported in adult-onset Still’s disease (AOSD). This study investigated the metabolomic profiles in AOSD patients and examined their association with clinical characteristics and disease outcome.

Methods

Serum metabolite profiles were determined on 32 AOSD patients and 30 healthy controls (HC) using ultra-performance liquid chromatography (UPLC)/MS analysis, and the differentially expressed metabolites were quantified using multiple reactions monitoring (MRM)/MS analysis in 44 patients and 42 HC. Pure standards were utilized to confirm the presence of the differentially expressed metabolites.

Results

Eighteen differentially expressed metabolites were identified in AOSD patents using LC/MS-based analysis, of which 13 metabolites were validated by MRM/MS analysis. Among them, serum levels of lysoPC(18:2), urocanic acid and indole were significantly lower, and L-phenylalanine levels were significantly higher in AOSD patients compared with HC. Moreover, serum levels of lysoPC(18:2), PhePhe, uridine, taurine, L-threonine, and (R)-3-Hydroxy-hexadecanoic acid were significantly correlated with disease activity scores (all p<0.05) in AOSD patients. A different clustering of metabolites was associated with a different disease outcome, with significantly lower levels of isovalerylsarcosine observed in patients with chronic articular pattern (median, 77.0AU/ml) compared with monocyclic (341.5AU/ml, p<0.01) or polycyclic systemic pattern (168.0AU/ml, p<0.05).

Conclusion

Thirteen differentially expressed metabolites identified and validated in AOSD patients were shown to be involved in five metabolic pathways. Significant associations of metabolic profiles with disease activity and outcome of AOSD suggest their involvement in AOSD pathogenesis.