Metabolomics in pediatric lower respiratory tract infections and sepsis: a literature review.

Lower respiratory tract infections (LRTIs) are a leading cause of morbidity and mortality in children. The ability of healthcare providers to diagnose and prognose LRTIs in the pediatric population remains a challenge, as children can present with similar clinical features regardless of the underlying pathogen or ultimate severity. Metabolomics, the large-scale analysis of metabolites and metabolic pathways offers new tools and insights that may aid in diagnosing and predicting the outcomes of LRTIs in children. This review highlights the latest literature on the clinical utility of metabolomics in providing care for children with bronchiolitis, pneumonia, COVID-19, and sepsis. IMPACT: This article summarizes current metabolomics approaches to diagnosing and predicting the course of pediatric lower respiratory infections. This article highlights the limitations to current metabolomics research and highlights future directions for the field.

Comparison of Two Automated Targeted Metabolomics Programs to Manual Profiling by an Experienced Spectroscopist for 1H-NMR Spectra.

Automated programs that carry out targeted metabolite identification and quantification using proton nuclear magnetic resonance spectra can overcome time and cost barriers that limit metabolomics use. However, their performance needs to be comparable to that of an experienced spectroscopist. A previously analyzed pediatric sepsis data set of serum samples was used to compare results generated by the automated programs rDolphin and BATMAN with the results obtained by manual profiling for 58 identified metabolites. Metabolites were selected using Student's t-tests and evaluated with several performance metrics. The manual profiling results had the highest performance metrics values, especially for sensitivity (76.9%), area under the receiver operating characteristic curve (0.90), precision (62.5%), and testing accuracy based on a neural net (88.6%). All three approaches had high specificity values (77.7-86.7%). Manual profiling by an expert spectroscopist outperformed two open-source automated programs, indicating that further development is needed to achieve acceptable performance levels.

Metabolomics and Inflammatory Mediator Profiling for the Differentiation of Life-Threatening and Non-Severe Appendicitis in the Pediatric Population.

While children with appendicitis often have excellent clinical outcomes, some develop life-threatening complications including sepsis and organ dysfunction requiring pediatric intensive care unit (PICU) support. Our study applied a metabolomics and inflammatory protein mediator (IPM) profiling approach to determine the bio-profiles of children who developed severe appendicitis compared with those that did not. We performed a prospective case-control study of children aged 0-17 years with a diagnosis of appendicitis. Cases had severe disease resulting in PICU admission. Primary controls had moderate appendicitis (perforation without PICU); secondary controls had mild appendicitis (non-perforated). Serum samples were analyzed using Proton Nuclear Magnetic Resonance (1H NMR) Spectroscopy and Gas Chromatography-Mass Spectrometry (GC-MS); IPM analysis was performed using plasma bead-based multiplex profiling. Comparisons were made using multivariate data statistical analysis. Fifty-three children were included (15 severe, 38 non-severe). Separation between severe and moderate appendicitis demonstrated excellent sensitivity and specificity (100%, 88%; 14 compounds), separation between severe and mild appendicitis also showed excellent sensitivity and specificity (91%, 90%; 16 compounds). Biomarker patterns derived from metabolomics and IPM profiling are capable of distinguishing children with severe appendicitis from those with less severe disease. These findings provide an important first step towards developing non-invasive diagnostic tools for clinicians in early identification of children who are at a high risk of developing severe appendicitis.

Novel molecular biomarkers and diagnosis of acute appendicitis in children.

Reliable and efficient diagnosis of pediatric appendicitis is essential for the establishment of a clinical management plan and improvement of patient outcomes. Current strategies used to diagnose a child presenting with a suspected appendicitis include laboratory studies, clinical scores and diagnostic imaging. Although these modalities work in conjunction with each other, one optimal diagnostic strategy has yet to be agreed upon. The recent introduction of precision medicine techniques such as genomics, transcriptomics, proteomics and metabolomics has increased both the diagnostic sensitivity and specificity of appendicitis. Using these novel strategies, the integration of precision medicine into clinical practice via point-of-care technologies is a plausible future. These technologies would assist in the screening, diagnosis and prognosis of pediatric appendicitis.

Metabolic Framework for the Improvement of Autism Spectrum Disorders by a Modified Ketogenic Diet: A Pilot Study.

The ketogenic diet (KD) can improve the core features of autism spectrum disorders (ASD) in some children, but the effects on the overall metabolism remain unclear. This pilot study investigated the behavioral parameters in relation to blood metabolites and trace elements in a cohort of 10 typically developed controls (TC) and 17 children with ASD at baseline and following 3 months of treatment with a modified KD regimen. A nontargeted, multiplatform metabolomic approach was employed, including gas chromatography-mass spectrometry, 1H nuclear magnetic resonance spectroscopy, and inductively coupled plasma-mass spectrometry. The associations among plasma metabolites, trace elements, and behavior scores were investigated. Employing a combination of metabolomic platforms, 118 named metabolites and 73 trace elements were assessed. Relative to TC, a combination of glutamate, galactonate, and glycerol discriminated ASD with 88% accuracy. ASD had higher concentrations of galactose intermediates, gut microbe-derived trimethylamine N-oxide and N-acetylserotonin, and lower concentrations of 3-hydroxybutyrate and selenium at baseline. Following 3 months of KD intervention, the levels of circulating ketones and acetylcarnitine were increased. KD restored lower selenium levels in ASD to that of controls, and correlation analysis identified a novel negative correlation between the changes in selenium and behavior scores. Based on the different behavior responses to KD, we found that high responders had greater concentrations of 3-hydroxybutyrate and ornithine, with lower galactose. These findings enhance our current understanding of the metabolic derangements present in ASD and may be of utility in predicting favorable responses to KD intervention.

Distinct Gut Microbiota and Serum Metabolites in Response to Weight Loss Induced by Either Dairy or Exercise in a Rodent Model of Obesity.

Energy imbalance is a primary cause of obesity. While the classical approach to attenuate weight gain includes an increase in energy expenditure through exercise, dietary manipulation such as the inclusion of dairy products has also been proven effective. In the present study, we explored the potential mechanisms by which dairy and exercise attenuate weight gain in diet-induced obese rats. Male Sprague-Dawley rats were fed a high fat, high-sugar (HFHS) diet to induce obesity for 8 weeks. Rats were then further grouped into either control (HFHS + casein) or dairy diet (HFHS + nonfat skim milk) with and without treadmill exercise for 6 weeks. Serum and fresh fecal samples were collected for gut microbiota, serum metabolomics, and metallomics analysis. Diet and exercise resulted in distinct separation in both gut microbiota and serum metabolite profiles. Most intriguingly, obesogenic bacteria including Desulfovibrio and Oribacterium were reduced, and bioactive molecules such as mannose and arginine were significantly increased in the dairy group. Correlations of at least six bacterial genera with serum metal ions and metabolites were also found. Results reveal distinct impacts of dairy and exercise on the gut microbiota and in the modulation of circulating metabolites with the former primarily responsible for driving microbial alterations known to attenuate weight gain.

Metabolite Profiling of Clinical Cancer Biofluid Samples by NMR Spectroscopy.

Metabolomics is a comprehensive characterization of the small polar molecules (metabolites) in different biological systems. One of the analytical platforms commonly used to study metabolic alterations in biofluid samples is proton nuclear magnetic resonance (1H NMR) spectroscopy. NMR spectroscopy is very specific, quantitative, and highly reproducible. Moreover, sample preparation for NMR experiments is very simple and straightforward, and this gives NMR spectroscopy a distinct advantage over other metabolic profiling methods. It has already been shown that 1H NMR-based profiling of biological fluids can be effective in differentiating benign from malignant lesions and in investigating the efficacy of specific cancer treatments. Therefore, 1H NMR spectroscopy may become a promising tool for early noninvasive diagnosis and rapid assessment of treatment effects in cancer patients. Here, we describe a detailed protocol for 1H NMR metabolite profiling in serum, plasma, and urine samples, including sample collection procedures, sample preparation for 1H NMR experiments, spectral acquisition and processing, and quantitative profiling of 1H NMR spectra. We also discuss several aspects of appropriate study design and some multivariate statistical methods that are commonly used to analyze metabolomics datasets.

Impact of dietary fiber supplementation on modulating microbiota-host-metabolic axes in obesity.

Low dietary fiber intake is associated with higher rates of microbiota-associated chronic diseases such as obesity. Low-fiber diets alter not only microbial composition but also the availability of metabolic end products derived from fermentation of fiber. Our objective was to examine the effects of dietary fiber supplementation on gut microbiota and associated fecal and serum metabolites in relation to metabolic markers of obesity. We conducted a 12-week, single-center, double-blind, placebo-controlled trial with 53 adults with overweight or obesity. They were randomly assigned to a pea fiber (PF, 15 g/d in wafer form; n=29) or control (CO, isocaloric amount of wafers; n=24) group. Blood and fecal samples were collected at baseline and 12 weeks. Serum metabolomics, gut microbiota and fecal short-chain fatty acids (SCFAs) and bile acids (BAs) were examined. Within-group but not between-group analysis showed a significant effect of treatment on serum metabolites at 12 weeks compared to baseline. Fiber significantly altered fecal SCFAs and BAs with higher acetate and reduced isovalerate, cholate, deoxycholate and total BAs content in the PF group compared to baseline. Microbiota was differentially modulated in the two groups, including an increase in the SCFA producer Lachnospira in the PF group and decrease in the CO group. The change in body weight of participants showed a negative correlation with their change in Lachnospira (r=-0.463, P=.006) abundance. The current study provides insight into the actions of pea fiber and its impact on modulating microbiota-host-metabolic axes in obesity.

Biomarker Phenotype for Early Diagnosis and Triage of Sepsis to the Pediatric Intensive Care Unit.

Early diagnosis and triage of sepsis improves outcomes. We aimed to identify biomarkers that may advance diagnosis and triage of pediatric sepsis. Serum and plasma samples were collected from young children (1-23 months old) with sepsis on presentation to the Pediatric Intensive Care Unit (PICU-sepsis, n = 46) or Pediatric Emergency Department (PED-sepsis, n = 58) and PED-non-sepsis patients (n = 19). Multivariate analysis was applied to distinguish between patient groups. Results were compared to our results for older children (2-17 years old). Common metabolites and protein-mediators were validated as potential biomarkers for a sepsis-triage model to differentiate PICU-sepsis from PED-sepsis in children age 1 month-17 years. Metabolomics in young children clearly separated the PICU-sepsis and PED-sepsis cohorts: sensitivity 0.71, specificity 0.93, and AUROC = 0.90 ± 0.03. Adding protein-mediators to the model did not improve performance. The seven metabolites common to the young and older children were used to create the sepsis-triage model. Validation of the sepsis-triage model resulted in sensitivity: 0.83 ± 0.02, specificity: 0.88 ± 0.05 and AUROC 0.93 ± 0.02. The metabolic-based biomarkers predicted which sepsis patients required care in a PICU versus those that could be safely cared for outside of a PICU. This has potential to inform appropriate triage of pediatric sepsis, particularly in EDs with less experience evaluating children.

Sarcopenia and myosteatosis are accompanied by distinct biological profiles in patients with pancreatic and periampullary adenocarcinomas.

INTRODUCTION: Pancreatic and periampullary adenocarcinomas are associated with abnormal body composition visible on CT scans, including low muscle mass (sarcopenia) and low muscle radiodensity due to fat infiltration in muscle (myosteatosis). The biological and clinical correlates to these features are poorly understood. METHODS: Clinical characteristics and outcomes were studied in 123 patients who underwent pancreaticoduodenectomy for pancreatic or non-pancreatic periampullary adenocarcinoma and who had available preoperative CT scans. In a subgroup of patients with pancreatic cancer (n = 29), rectus abdominus muscle mRNA expression was determined by cDNA microarray and in another subgroup (n = 29) 1H-NMR spectroscopy and gas chromatography-mass spectrometry were used to characterize the serum metabolome. RESULTS: Muscle mass and radiodensity were not significantly correlated. Distinct groups were identified: sarcopenia (40.7%), myosteatosis (25.2%), both (11.4%). Fat distribution differed in these groups; sarcopenia associated with lower subcutaneous adipose tissue (P<0.0001) and myosteatosis associated with greater visceral adipose tissue (P<0.0001). Sarcopenia, myosteatosis and their combined presence associated with shorter survival, Log Rank P = 0.005, P = 0.06, and P = 0.002, respectively. In muscle, transcriptomic analysis suggested increased inflammation and decreased growth in sarcopenia and disrupted oxidative phosphorylation and lipid accumulation in myosteatosis. In the circulating metabolome, metabolites consistent with muscle catabolism associated with sarcopenia. Metabolites consistent with disordered carbohydrate metabolism were identified in both sarcopenia and myosteatosis. DISCUSSION: Muscle phenotypes differ clinically and biologically. Because these muscle phenotypes are linked to poor survival, it will be imperative to delineate their pathophysiologic mechanisms, including whether they are driven by variable tumor biology or host response.

Serum Metabolite Profiles Are Altered by Erlotinib Treatment and the Integrin α1-Null Genotype but Not by Post-Traumatic Osteoarthritis.

The risk of developing post-traumatic osteoarthritis (PTOA) following joint injury is high. Furthering our understanding of the molecular mechanisms underlying PTOA and/or identifying novel biomarkers for early detection may help to improve treatment outcomes. Increased expression of integrin α1ß1 and inhibition of epidermal growth factor receptor (EGFR) signaling protect the knee from spontaneous OA; however, the impact of the integrin α1ß1/EGFR axis on PTOA is currently unknown. We sought to determine metabolic changes in serum samples collected from wild-type and integrin α1-null mice that underwent surgery to destabilize the medial meniscus and were treated with the EGFR inhibitor erlotinib. Following (1)H nuclear magnetic resonance spectroscopy, we generated multivariate statistical models that distinguished between the metabolic profiles of erlotinib- versus vehicle-treated mice and the integrin α1-null versus wild-type mouse genotype. Our results show the sex-dependent effects of erlotinib treatment and highlight glutamine as a metabolite that counteracts this treatment. Furthermore, we identified a set of metabolites associated with increased reactive oxygen species production, susceptibility to OA, and regulation of TRP channels in α1-null mice. Our study indicates that systemic pharmacological and genetic factors have a greater effect on serum metabolic profiles than site-specific factors such as surgery.

Development of metabolic and inflammatory mediator biomarker phenotyping for early diagnosis and triage of pediatric sepsis.

INTRODUCTION: The first steps in goal-directed therapy for sepsis are early diagnosis followed by appropriate triage. These steps are usually left to the physician's judgment, as there is no accepted biomarker available. We aimed to determine biomarker phenotypes that differentiate children with sepsis who require intensive care from those who do not. METHODS: We conducted a prospective, observational nested cohort study at two pediatric intensive care units (PICUs) and one pediatric emergency department (ED). Children ages 2-17 years presenting to the PICU or ED with sepsis or presenting for procedural sedation to the ED were enrolled. We used the judgment of regional pediatric ED and PICU attending physicians as the standard to determine triage location (PICU or ED). We performed metabolic and inflammatory protein mediator profiling with serum and plasma samples, respectively, collected upon presentation, followed by multivariate statistical analysis. RESULTS: Ninety-four PICU sepsis, 81 ED sepsis, and 63 ED control patients were included. Metabolomic profiling revealed clear separation of groups, differentiating PICU sepsis from ED sepsis with accuracy of 0.89, area under the receiver operating characteristic curve (AUROC) of 0.96 (standard deviation [SD] 0.01), and predictive ability (Q(2)) of 0.60. Protein mediator profiling also showed clear separation of the groups, differentiating PICU sepsis from ED sepsis with accuracy of 0.78 and AUROC of 0.88 (SD 0.03). Combining metabolomic and protein mediator profiling improved the model (Q(2) =0.62), differentiating PICU sepsis from ED sepsis with accuracy of 0.87 and AUROC of 0.95 (SD 0.01). Separation of PICU sepsis or ED sepsis from ED controls was even more accurate. Prespecified age subgroups (2-5 years old and 6-17 years old) improved model accuracy minimally. Seventeen metabolites or protein mediators accounted for separation of PICU sepsis and ED sepsis with 95% confidence. CONCLUSIONS: In children ages 2-17 years, combining metabolomic and inflammatory protein mediator profiling early after presentation may differentiate children with sepsis requiring care in a PICU from children with or without sepsis safely cared for outside a PICU. This may aid in making triage decisions, particularly in an ED without pediatric expertise. This finding requires validation in an independent cohort.

Integration of metabolic and inflammatory mediator profiles as a potential prognostic approach for septic shock in the intensive care unit.

INTRODUCTION: Septic shock is a major life-threatening condition in critically ill patients and it is well known that early recognition of septic shock and expedient initiation of appropriate treatment improves patient outcome. Unfortunately, to date no single compound has shown sufficient sensitivity and specificity to be used as a routine biomarker for early diagnosis and prognosis of septic shock in the intensive care unit (ICU). Therefore, the identification of new diagnostic tools remains a priority for increasing the survival rate of ICU patients. In this study, we have evaluated whether a combined nuclear magnetic resonance spectroscopy-based metabolomics and a multiplex cytokine/chemokine profiling approach could be used for diagnosis and prognostic evaluation of septic shock patients in the ICU. METHODS: Serum and plasma samples were collected from septic shock patients and ICU controls (ICU patients with the systemic inflammatory response syndrome but not suspected of having an infection). (1)H Nuclear magnetic resonance spectra were analyzed and quantified using the targeted profiling methodology. The analysis of the inflammatory mediators was performed using human cytokine and chemokine assay kits. RESULTS: By using multivariate statistical analysis we were able to distinguish patient groups and detect specific metabolic and cytokine/chemokine patterns associated with septic shock and its mortality. These metabolites and cytokines/chemokines represent candidate biomarkers of the human response to septic shock and have the potential to improve early diagnosis and prognosis of septic shock. CONCLUSIONS: Our findings show that integration of quantitative metabolic and inflammatory mediator data can be utilized for the diagnosis and prognosis of septic shock in the ICU.

Metabolic analysis of knee synovial fluid as a potential diagnostic approach for osteoarthritis.

Osteoarthritis (OA) is a leading cause of chronic joint pain in the older human population. Diagnosis of OA at an earlier stage may enable the development of new treatments to one day effectively modify the progression and prognosis of the disease. In this work, we explore whether an integrated metabolomics approach could be utilized for the diagnosis of OA. Synovial fluid (SF) samples were collected from symptomatic chronic knee OA patients and normal human cadaveric knee joints. The samples were analyzed using (1)H nuclear magnetic resonance (NMR) spectroscopy and gas chromatography-mass spectrometry (GC-MS) followed by multivariate statistical analysis. Based on the metabolic profiles, we were able to distinguish OA patients from the controls and validate the statistical models. Moreover, we have integrated the (1)H NMR and GC-MS results and we found that 11 metabolites were statistically important for the separation between OA and normal SF. Additionally, statistical analysis showed an excellent predictive ability of the constructed metabolomics model (area under the receiver operating characteristic curve = 1.0). Our findings indicate that metabolomics might serve as a promising approach for the diagnosis and prognosis of degenerative changes in the knee joint and should be further validated in clinical settings.

Metabolic profiling of synovial fluid in a unilateral ovine model of anterior cruciate ligament reconstruction of the knee suggests biomarkers for early osteoarthritis.

Joint injuries and subsequent osteoarthritis (OA) are the leading causes of chronic joint disease. In this work, we explore the possibility of applying magnetic resonance spectroscopy-based metabolomics to detect host responses to an anterior cruciate ligament (ACL) reconstruction injury in synovial fluid in an ovine model. Using multivariate statistical analysis, we were able to distinguish post-injury joint samples (ACL and sham surgery) from the uninjured control samples, and as well the ACL surgical samples from sham surgery. In all samples there were 65 metabolites quantified, of which six could be suggested as biomarkers for early post-injury degenerative changes in the knee joints: isobutyrate, glucose, hydroxyproline, asparagine, serine, and uridine. Our results raise a cautionary note indicating that surgical interventions into the knee can result in metabolic alterations that need to be distinguished from those caused by the early onset of OA. Our findings illustrate the potential application of metabolomics as a diagnostic and prognostic tool for detection of injuries to the knee joint. The ability to detect a unique pattern of metabolic changes in the synovial fluid of sheep offers the possibility of extending the approach to precision medicine protocols in patient populations in the future.

Metabolic changes associated with selenium deficiency in mice.

Selenium (Se), which is a central component for the biosynthesis and functionality of selenoproteins, plays an important role in the anti-oxidative response, reproduction, thyroid hormone metabolism and the protection from infection and inflammation. However, dietary Se effects have not well been established to date and the available studies often present contradictory results. To obtain a better understanding of Se intake and its influence on the metabolism of living systems, we have utilized a metabolomics approach to gain insight into the specific metabolic alterations caused by Se deficiency in mice. Serum samples were collected from two groups of C57BL/6 mice: an experimental group which was fed a Se-deficient diet and controls consuming normal chow. The samples were analyzed by (1)H nuclear magnetic resonance spectroscopy and gas chromatography-mass spectrometry. The resulting metabolite data were examined separately for both analytical methods and in a combined manner. By applying multivariate statistical analysis we were able to distinguish the two groups and detect a metabolite pattern associated with Se deficiency. We found that the concentrations of 15 metabolites significantly changed in serum samples collected from Se-deficient mice when compared to the controls. Many of the perturbed biological pathways pointed towards compensatory mechanisms during Se deficiency and were associated with amino acid metabolism. Our findings show that a metabolomics approach may be applied to identify the metabolic impact of Se and reveal the most impaired biological pathways as well as induced regulatory mechanisms during Se deficiency.

Metabolomics in critical care medicine: a new approach to biomarker discovery.

PURPOSE: To present an overview and comparison of the main metabolomics techniques (1H NMR, GC-MS, and LC-MS) and their current and potential use in critical care medicine. SOURCE: This is a focused review, not a systematic review, using the PubMed database as the predominant source of references to compare metabolomics techniques. PRINCIPAL FINDINGS: 1H NMR, GC-MS, and LC-MS are complementary techniques that can be used on a variety of biofluids for metabolomics analysis of patients in the Intensive Care Unit (ICU). These techniques have been successfully used for diagnosis and prognosis in the ICU and other clinical settings; for example, in patients with septic shock and community-acquired pneumonia. CONCLUSION: Metabolomics is a powerful tool that has strong potential to impact diagnosis and prognosis and to examine responses to treatment in critical care medicine through diagnostic and prognostic biomarker and biopattern identification.

Metabolic profiling of serum samples by 1H nuclear magnetic resonance spectroscopy as a potential diagnostic approach for septic shock.

OBJECTIVES: To determine whether a nuclear magnetic resonance-based metabolomics approach can be useful for the early diagnosis and prognosis of septic shock in ICUs. DESIGN: Laboratory-based study. SETTING: University research laboratory. SUBJECTS: Serum samples from septic shock patients and ICU controls (ICU patients with systemic inflammatory response syndrome but not suspected of having an infection) were collected within 24 hours of admittance to the ICU. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: H nuclear magnetic resonance spectra of septic shock and ICU control samples were analyzed and quantified using a targeted profiling approach. By applying multivariate statistical analysis (e.g., orthogonal partial least squares discriminant analysis), we were able to distinguish the patient groups and detect specific metabolic patterns. Some of the metabolites were found to have a significant impact on the separation between septic shock and control samples. These metabolites could be interpreted in terms of a biological human response to septic shock and they might serve as a biomarker pattern for septic shock in ICUs. Additionally, nuclear magnetic resonance-based metabolomics was evaluated in order to detect metabolic variation between septic shock survivors and nonsurvivors and to predict patient outcome. The area under the receiver operating characteristic curve indicated an excellent predictive ability for the constructed orthogonal partial least squares discriminant analysis models (septic shock vs ICU controls: area under the receiver operating characteristic curve = 0.98; nonsurvivors vs survivors: area under the receiver operating characteristic curve = 1). CONCLUSIONS: Our results indicate that nuclear magnetic resonance-based metabolic profiling could be used for diagnosis and mortality prediction of septic shock in the ICU.

Metabolomics as a novel approach for early diagnosis of pediatric septic shock and its mortality.

RATIONALE: Septic shock is a significant cause of morbidity and mortality in the pediatric population. Early recognition of septic shock and appropriate treatment increase survival rate; thus, developing new diagnostic tools may improve patients' outcomes. OBJECTIVES: To determine whether a metabolomics approach could be useful in the diagnosis and prognosis of septic shock in pediatric intensive care unit (PICUs). METHODS: Serum samples were collected from 60 patients with septic shock, 40 PICU patients with systemic inflammatory response syndrome (not suspected of having an infection), and 40 healthy children. Proton nuclear magnetic resonance spectroscopy spectra were analyzed and quantified using targeted profiling methodology. MEASUREMENTS AND MAIN RESULTS: Multivariate statistical analysis was applied to detect specific patterns in metabolic profiles and to highlight differences between patient samples. Supervised analysis afforded good predictive models and managed to separate patient populations. Some of the metabolite concentrations identified in serum samples changed markedly, indicating their influence on the separation between patient groups. These metabolites represent a composite biopattern of the pediatric metabolic response to septic shock and might be considered as the basis for a biomarker panel for the diagnosis of septic shock and its mortality in PICU. CONCLUSIONS: Our results indicate that nuclear magnetic resonance metabolite profiling might serve as a promising approach for the diagnosis and prediction of mortality in septic shock in a pediatric population and that quantitative metabolomics methods can be applied in the clinical evaluations of pediatric septic shock.