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. 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. 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. 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.

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). ¹H 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.

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 (Q²) 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 (Q² =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.

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.

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.