Beata Mickiewicz

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:1H 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.

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

IntroductionSeptic 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.MethodsSerum 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.ResultsBy 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.ConclusionsOur 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 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 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 1H 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 1H 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.

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

IntroductionThe 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.MethodsWe 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.ResultsNinety-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.ConclusionsIn 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.

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 α1β1 and inhibition of epidermal growth factor receptor (EGFR) signaling protect the knee from spontaneous OA; however, the impact of the integrin α1β1/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.

Metabolic profile of plasma before and after induction of an isolated intra-articular bone injury in the rabbit knee: Potential to characterize the onset of osteoarthritis?

Background: Surgical models of bone injury-induced joint damage provide relevant insights into the biological pathways involved in the response to injury and development of subsequent degenerative joint conditions. Objective: To determine metabolic changes acutely following a bone injury to the rabbit knee in order to reveal key metabolites potentially associated with the chronic phase post-injury leading to post-traumatic osteoarthritis. Methods: Nine skeletally mature rabbits underwent surgery to create a repeatable, isolated intra-articular bone injury with intraarticular bleeding, without destabilizing the knee. Plasma samples were collected pre-operatively (baseline) and at 3 weeks post-injury. The samples were analyzed using nuclear magnetic resonance spectroscopy-based metabolic profiling approach and multivariate statistical analysis. Results: Metabolic profiling found clear separation between pre-surgical and post-injury rabbits. The predictive ability of the statistical model reached 75%. The levels of twelve metabolites (adenine, choline, glutamine, glycine, pyroglutamate, ornithine, 1-methylhistidine, creatinine, acetate, glucose, taurine and glutamate) significantly changed in plasma samples collected from the rabbits 3 weeks post-injury compared to their baseline levels. Conclusions: Our study indicates that metabolomics may have important applications in the detection of early systemic changes following a localized joint injury, possibly enabling early intervention and preventing progression to more serious joint degeneration.