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Urine Metabolomics in Hemorrhagic Shock: Normalization of Urine in the Face of Changing Intravascular Fluid Volume and Perturbations in Metabolism
Abstract
There are many ways to normalize biofluid metabolomics data to account for changes in dilution, all of which have been thoroughly examined in model systems. Here, urine metabolomics data was examined under relevant physiological conditions obtained from a porcine model of hemorrhagic shock and resuscitation. This includes highly variable intravascular fluid volume and urine output coupled with large perturbations in the abundance of endogenous metabolites. Seven different normalization techniques and raw data were evaluated to determine an appropriate normalization technique in this setting, including spectral post-processing methods and physiological measures of concentration. Relationships between normalization constants for each urine sample were examined, as well as relationships between urinary and serum creatinine concentrations. Principal components analysis was used to examine clustering of metabolomics data. The set of normalization constants associated with each sample were reflective of urine concentration, with a trend toward concentration decreases during late resuscitation timepoints. Urinary creatinine normalized to urine output was most reflective of serum creatinine levels. Principal components analysis showed that urine samples clustered according to experimental timepoint for all normalization methods examined. Little separation was seen in raw data. Urine output-normalized data stands out from the six other normalization methods studied because it is reflective of renal clearance and should be used when comparing urine and serum metabolomics data.
... In this paper, we examine the effect of normalization on the structure of metabolomic data; this interest is certainly not new and has been addressed in the context of exploratory and predictive data analysis 5,6 and biomarker selection. 5,7 However, in the present study, we analyze in detail how correlation patterns change upon normalization of the data (considering previously investigated methods), 5,8,9 and we show how this can affect PCA, partial least-squares discriminant analysis (PLS-DA), and the inference of metabolite−metabolite association networks. ...
... y e (7) where y is the vector of metabolite concentrations, d is the dilution factor, and e describes the experimental noise. This can be generalized to a set of n × p samples using the matrix expression = + X DY E (8) where D is a p × p diagonal containing the dilution factors, one for each sample. The matrix Y was generated under a multivariate normal distribution ...
... The effects of normalization on PCA results were discussed in. 6,8 On the basis of the inspection of the score plots, differences were observed in the PCA model obtained from data to which different normalization procedures were applied. However, PCA models should be compared in terms of the loadings and scores because the loadings define the model, and it is through inspection of the loading that the model should be interpreted. ...
Normalization is a fundamental step in data processing to account for the sample-to-sample variation observed in biological samples. However, data structure is affected by normalization. In this paper we show how, and to what extent, the correlation structure is affected by the application of 11 different normalization procedures and we discuss the consequences for data analysis and interpretation including principal component analysis, partial least squares discrimination, and the inference of metabolite–metabolite association networks.
... Creatinine is an abundant urinary metabolite that correlates with overall body size (and muscle mass) and exhibits little excretory variation in healthy individuals. Creatinine is therefore a useful internal standard in urine, and can be used to account for variances in urine water content in most healthy individuals (Lusczek et al. 2001;Mpio et al. 2003;Saude et al. 2007;Temmerman et al. 2012). Creatinine normalization of human urinary metabolites is also commonly used in clinical practice to control for variations in urine flow rate/glomerular filtration rate (GFR) (Waikar et al. 2010). ...
... GFR declines steadily with age, and disease progression, such as in diabetic individuals, affects urinary creatinine output. However, creatinine can serve as a useful metabolite normalization factor for human metabolites to correct for individual dilution effects where GFR is not impaired (Lusczek et al. 2001;Mpio et al. 2003;Myers 2003;Saude et al. 2007). As no significant difference in average urinary creatinine concentrations were observed between the BL group and C group, we conclude no significant differences in GFR were apparent amongst the study cohort over the 18-month period. ...
The musculoskeletal benefits of calcium and vitamin-D3 supplementation and exercise have been extensively studied, but the effect on metabolism remains contentious. Urine samples were analyzed by 1H-NMR spectroscopy from participants recruited for an 18-month, randomized controlled trial of a multi-component exercise program and calcium and vitamin-D3 fortified milk consumption. It was shown previously that no increase in musculoskeletal composition was observed for participants assigned to the calcium and vitamin-D3 intervention, but exercise resulted in increased bone mineral density, total lean body mass and muscle strength. Retrospective metabolomics analysis of urine samples from patients involved in this study revealed no distinct changes in the urinary metabolome in response to the calcium and vitamin-D3 intervention, but significant changes followed the exercise intervention, notably a reduction in creatinine and an increase in choline, guanidinoacetate, and hypoxanthine (p<0.001, fold change>1.5). These metabolites are intrinsically involved in anaerobic ATP synthesis, intracellular buffering and methyl-balance regulation. The exercise intervention had a marked effect on the urine metabolome and markers of muscle turnover but none of these metabolites were obvious markers of bone turnover. Measurement of specific urinary exercise biomarkers may provide a basis for monitoring performance and metabolic response to exercise regimes.
... The ''FR'' label designates ''full resuscitation''; for instance, the FR8 label indicates 8 h of full resuscitation have been administered. The identification of the FR2 timepoint as ''early resuscitation'' and the FR8 and FR20 timepoints as ''late resuscitation'' follows from (Lusczek et al. 2011) Urinary metabolic network analysis 225 Z-gradient, running TopSpin v. 2.16 (Bruker BioSpin, Fremont, CA USA) at 700.13 MHz. A 1D NOESY (Nuclear Overhauser Effect Spectroscopy) pulse sequence was used to collect spectra of each sample. ...
... Metabolite concentrations were multiplied by urine output (cc/hour/kg) to correct for changes in the concentration of urine throughout the experiment (Lusczek et al. 2011). Urine output was not available at experimental endpoint (48 h after the initiation of shock), thus these data were omitted from the analysis. ...
Hemorrhagic shock, often a result of traumatic injury, is a condition of reduced perfusion that results in diminished delivery of oxygen to tissues. The disruption in oxygen delivery induced by both ischemia (diminished oxygen delivery) and reperfusion (restoration of oxygen delivery) has profound consequences for cellular metabo-lism and the maintenance of homeostasis. The pathophys-iologic state associated with traumatic injury and hemorrhagic shock was studied with a scale-invariant metabolic network. Urinary metabolic profiles were con-structed from NMR spectra of urine samples collected at set timepoints in a porcine model of hemorrhagic shock that included a pulmonary contusion, a liver crush injury, and a 35 % controlled bleed. The network was constructed from these metabolic profiles. A partial least squares dis-criminant analysis (PLS-DA) model that discriminates by experimental timepoint was also constructed. Comparisons of the network (functional relationships among metabo-lites) and PLS-DA model (observable relationships to experimental time course) revealed complementary infor-mation. First, ischemia/reperfusion injury and evidence of cell death due to hemorrhage was associated with early resuscitation timepoints. Second, evidence of increased protein catabolism and traumatic injury was associated with late resuscitation timepoints. These results are concordant with generally accepted views of the metabolic progression of shock.
... For the urine, metabolite concentrations were divided by the osmolality of each urine sample (millimoles of solute per liter of urine) to correct for changes in the concentration of urine at all timepoints. 22 Urea was removed from the urine data set because its signal is compromised by the NOESY pulse sequence. ...
Objectives:
Metabolomics-based diagnosis or prediction of risk may improve patient outcomes and improve understanding of the pathogenesis of acute pancreatitis (AP). Endoscopic retrograde cholangiopancreatography (ERCP) is a risk factor for developing AP. This pilot study examined metabolomes of patients before and after ERCP, hypothesizing that metabolomics could differentiate between patients who did and did not develop post-ERCP pancreatitis, and that biomarkers associated with development of AP could be identified.
Methods:
Patients at high risk for developing post-ERCP pancreatitis were prospectively enrolled at the University of Minnesota from October 2012 to February 2014. Urine and serum samples were collected before ERCP, 2 h after ERCP, and daily thereafter if patients were admitted to the hospital with AP. Pancreatitis severity was calculated with Bedside Index for Severity in Acute Pancreatitis (BISAP) and Modified Glasgow scores. Patients who developed AP (n=9) were matched to patients who did not develop AP (n=18) by age and gender. Urine and serum metabolites were profiled with nuclear magnetic resonance spectroscopy. Partial least squares discriminant analysis (PLS-DA) was performed to detect changes in metabolic profiles associated with development of pancreatitis. Metabolic networks were constructed to probe functional relationships among metabolites.
Results:
Of the 113 enrolled patients, 9 developed mild AP according to BISAP and modified Glasglow scores. PLS-DA showed common differences between pre- and post-ERCP metabolic profiles in urine and serum regardless of AP status, characterized by increases in serum and urine ketones and serum glucose. Pre-ERCP lipase levels were somewhat elevated in those who went on to develop AP, though this did not reach statistical significance. Metabolic networks differed between patients with AP and those without after ERCP; however, metabolomics did not identify specific prognostic or diagnostic markers of ERCP-induced AP. Aspartate and asparagine were identified as well-connected hubs in post-ERCP serum networks of cases and were correlated with aspartate transaminase (AST) and white blood cell count levels. These features were not evident in controls. Serum aspartate was elevated in AP patients relative to those without AP after ERCP (P=0.03).
Conclusions:
In this pilot study, ERCP was found to induce global changes in urine and serum metabolomes indicative of alterations in pancreatic function and insulin resistance. This should be taken into consideration in future research on this topic. Post-ERCP serum metabolic networks indicate functional differences surrounding aspartate metabolism between patients with AP and those without. Further study must be done in larger patient populations to test elevated lipase as a prognostic biomarker associated with risk of developing AP and to examine active metabolic mechanisms at work.
... Serum metabolites (n=53) are reported in mM. Urine metabolite concentrations (n=60) were multiplied by urine output (cc/hour/kg) to correct for changes in the concentration of urine throughout the experiment 21 . The final metabolite abundances (nmol/h/kg) were log-transformed (base 10) to allow for comparisons among metabolites over several orders of magnitude (range: 0-5.9 × 10 5 nmol/h/kg). ...
Hemorrhagic shock, a result of extensive blood loss, is a dominant factor in battlefield morbidity and mortality. Early rodent studies in hemorrhagic shock reported carbohydrate feeding prior to the induction of hemorrhagic shock decreased mortality. When repeated in our laboratory with a porcine model, carbohydrate pre-feed resulted in a nearly doubled death rate following hemorrhagic shock with trauma when compared to fasted animals. In an attempt to explain the unexpected death rate for pre-fed animals, we investigated the metabolic profiles of pre-fed non-survivors across 4 compartments (liver, muscle, serum, and urine) at specific time intervals (pre-shock, shock, and resuscitation) and compared them to pre-fed survivors. As hypothesized, pre-fed pigs that died as a result of hemorrhage and trauma showed differences in their metabolic and physiologic profiles at all time intervals and in all compartments when compared to pre-fed survivors. Our data suggest that, although all animals were subjected to the same shock and trauma protocol, non-survivors exhibited altered carbohydrate processing as early as the pre-shock sampling point. This was evident in (for example) the higher levels of ATP and markers of greater anabolic activity in the muscle at the pre-shock time point. Based on the metabolic findings, we propose two mechanisms that connect pre-fed status to a higher death rate: 1) animals that die are more susceptible to opening of the mitochondrial permeability transition pore, a major factor in ischemia/reperfusion injury; and 2) loss of fasting-associated survival mechanisms in pre-fed animals.
... Serum metabolites (53) are reported in mM. For the urine (60) metabolite concentrations were multiplied by urine output (cc/hour/kg) to correct for changes in the concentration of urine throughout the experiment [16]. The final urine metabolite abundances (nmol/h/kg) were log-transformed (base 10) to allow for comparisons among metabolites over several orders of magnitude (range: 0-5.9 × 10 5 nmol/h/kg). ...
Objective:
Hemorrhagic shock accompanied by injury represents a major physiologic stress. Fasted animals are often used to study hemorrhagic shock (with injury). A fasted state is not guaranteed in the general human population. The objective of this study was to determine if fed animals would exhibit a different metabolic profile in response to hemorrhagic shock with trauma when compared to fasted animals.
Methods:
Proton (1H) NMR spectroscopy was used to determine concentrations of metabolites from four different compartments (liver, muscle, serum, urine) taken at defined time points throughout shock/injury and resuscitation. PLS-DA was performed and VIP lists established for baseline, shock and resuscitation (10 metabolites for each compartment at each time interval) on metabolomics data from surviving animals.
Results:
Fed status prior to the occurrence of hemorrhagic shock with injury alters the metabolic course of this trauma and potentially affects mortality. The death rate for CPF animals is higher than FS animals (47 vs 28%). The majority of deaths occur post-resuscitation suggesting reperfusion injury. The metabolomics response to shock reflects priorities evident at baseline. FS animals raise the baseline degree of proteolysis to provide additional amino acids for energy production while CPF animals rely on both glucose and, to a lesser extent, amino acids. During early resuscitation levels of metabolites associated with energy production drop, suggesting diminished demand.
Conclusions:
Feeding status prior to the occurrence of hemorrhagic shock with injury alters the metabolic course of this trauma and potentially affects mortality. The response to shock reflects metabolic priorities at baseline.
... An already established model of porcine hemorrhagic shock was described (Lusczek et al. 2011;Scribner et al. 2010;Mulier et al. 2005). Briefly, hemorrhagic shock was induced on 8 male Yorkshire pigs (Manthei Hog Farm, LLC, Elk River, MN) while anesthetized; a blunt percussive injury to the chest was created using a captive bolt device followed by withdrawal of blood (35 %) from the inferior vena cava. ...
Identifying biomarkers that are indicative of a phenotypic state is difficult because of the amount of natural variability which exists in any population. While there are many different algorithms to select biomarkers, previous investigation shows the sensitivity and flexibility of support vector machines (SVM) make them an attractive candidate. Here we evaluate the ability of support vector machine recursive feature elimination (SVM-RFE) to identify potential metabolic biomarkers in liquid chromatography mass spectrometry untargeted metabolite datasets. Two separate experiments are considered, a low variance (low biological noise) prokaryotic stress experiment, and a high variance (high biological noise) mammalian stress experiment. For each experiment, the phenotypic response to stress is metabolically characterized. SVM-based classification and metabolite ranking is undertaken using a systematically reduced number of biological replicates to evaluate the impact of sample size on biomarker reproducibility and robustness. Our results indicate the highest ranked 1 % of metabolites, the most predictive of the physiological state, were identified by SVM-RFE even when the number of training examples was small (≥3) and the coefficient of variation was high (>0.5). An accuracy analysis shows filtering with recursive feature elimination measurably improves SVM classification accuracy, an effect that is pronounced when the number of training examples is small. These results indicate that SVM-RFE can be successful at biomarker identification even in challenging scenarios where the training examples are noisy and the number of biological replicates is low.
... Sixty metabolites were fit in each urine sample in this study, resulting in a profile containing the concentration of each identified metabolite in millimoles per liter (mM). The metabolomic profiles containing the urine concentrations were then normalized using the probabilistic quotient method [19,20] to correct for differences in dilution among samples. ...
The characterization of the urinary metabolome may yield biomarkers indicative of pancreatitis.
We establish a non-invasive technique to compare urinary metabolic profiles in patients with acute and chronic pancreatitis to healthy controls.
Urine was obtained from healthy controls (HC, n=5), inpatients with mild acute pancreatitis (AP, n=5), and outpatients with chronic pancreatitis (CP, n=5). Proton nuclear magnetic resonance spectra were obtained for each sample. Metabolites were identified and quantified in each spectrum; resulting concentrations were normalized to account for differences in dilution among samples. Kruskal-Wallis test, post-hoc Mann-Whitney U tests, and principal component analysis were performed to identify metabolites that discriminate healthy controls, acute pancreatitis, and chronic pancreatitis.
Sixty metabolites were identified and quantified; five were found to differ significantly (P<0.05) among the three groups. Of these, citrate and adenosine remained significant after validation by random permutation. Principal component analysis demonstrated that healthy control urine samples can be differentiated from patients with chronic pancreatitis or acute pancreatitis; chronic pancreatitis patients could not be distinguished from acute pancreatitis patients.
This metabolomic investigation demonstrates that this non-invasive technique offers insight into the metabolic states of pancreatitis. Although the identified metabolites cannot conclusively be defined as biomarkers of disease, future studies will validate our findings in larger patient cohorts.
This chapter overviews the advances in digital biosensors operating in the binary (YES/NO) mode, mostly emphasizing biomedical applications of enzyme‐based logic systems, particularly for the analysis of pathophysiological conditions associated with various injuries. The attractive performance of the new enzyme logic biosensors holds considerable promise for diverse practical and important future applications, including novel forensic and security applications. The chapter describes how the biomedical analysis can benefit from the use of biomolecular information processing (biocomputing) systems. Forensic analysis of biofluids can benefit from novel bioanalytical/biosensing methods, including those based on the biocomputing approach. The chapter also overviews the application of binary biosensing methods based on the enzyme logic gates to various types of injury biomarkers, particularly concentrating on battlefield injuries. It shows that the simplest logic processing based mostly on logic AND/OR gates, applied to biochemical input signals.
Objective
Treatment with a combination of d-β-hydroxybutyrate (BHB) and melatonin (M) improves survival in hemorrhagic shock models. Our objective was to find the most effective melatonin concentration in combination with 4 molar BHB (4 M BHB). Survival and markers of organ injury were analyzed in pigs exposed to pulmonary contusion, liver crush injury, and hemorrhagic shock and treated with lactated Ringer’s solution; 4 M BHB/43 mM M; 4 M BHB/20 mM M; 4 M BHB/10 mM M; 4 M BHB/4.3 mM M; or 4 M BHB/0.43 mM M. This work is an extension of a previously published research study.
Results
Survival was highest in pigs receiving 4 M BHB/43 mM M (13/14), followed by lactated Ringer’s solution (11/16) and BHB/M with decreased melatonin concentrations (4 M BHB/20 mM M 3/6, 4 M BHB/10 mM M 2/6, 4 M BHB/4.3 mM M 3/6, 4 M BHB/0.43 mM M 1/6, p = 0.011). High mortality was associated with increases in serum lactate, higher liver and muscle injury markers and decreases in PaO2:FiO2 ratios. Our study indicates that treatment with 4 M BHB and melatonin concentrations below 43 mM lack the survival benefit observed from 4 M BHB/43 mM melatonin in pigs experiencing hemorrhagic shock and polytrauma.
Electronic supplementary material
The online version of this article (10.1186/s13104-017-2975-0) contains supplementary material, which is available to authorized users.
Early recognition and intervention in hemorrhagic shock is essential to improved outcomes. However, the lack of robust diagnostic tools readily available to identify patients in the field inhibits the ability to provide timely intervention. Therefore, the development of a reliable prognostic indicator, such as a serum biomarker or a metabolic profile, has significant potential to improve far-forward trauma care. In this study, we used metabolomics as a tool to identify a metabolic state associated with the hemorrhagic shock and outcome in our porcine model of multiple injuries, shock, and resuscitation.
Proton nuclear magnetic resonance spectroscopy was used to evaluate serum metabolites from 23 animals that underwent multiple injuries, controlled hemorrhage, and 20 hours of a standard resuscitation protocol. Serum samples were collected from the animals at baseline (before hemorrhage), at shock (after 45 minutes of shock), and at 8 hours of full resuscitation.
We were able to demonstrate shifts in the metabolome throughout different time points and construct a metabolic profile associated with mortality using partial least squares discriminate analysis. The metabolites most responsible for the classification of hemorrhagic shock in our model serve as markers for ischemia, changes in energy production, and cellular damage. Hemorrhagic shock was characterized by marked increases in tricarboxylic acid cycle intermediates, glycolytic-gluconeogenic by-products, purine-pyrimidine catabolism, and fatty acid oxidation.
The results of this study demonstrate the potential for metabolomics as a tool to classify the metabolic flux, to identify relevant biochemical pathways, and to identify clinically useful biomarkers.
Novel biosensors based on the biocomputing concept digitally process multiple biochemical signals through Boolean logic networks of coupled biomolecular reactions and produce output in the form of a YES/NO response. Compared to traditional single-analyte sensing devices, biocomputing approach enables a high-fidelity multianalyte biosensing, particularly beneficial for biomedical applications.
A biocatalytic cascade for the analysis of the simultaneous increase in the concentration of two biomarkers characteristic of liver injury (alanine transaminase, ALT, and lactate dehydrogenase, LDH) was tested on real samples acquired from an animal model (domestic pigs, Sus scrofa domesticus) suffering from traumatic liver injury. A two-step reaction biocatalyzed in the presence of both enzyme-biomarkers resulted in the oxidation of NADH followed by optical absorbance measurements. A simple qualitative, YES/NO, test allowed for distinction between animals with and without the presence of liver injury with the probability of 92%. These data represent the first demonstration of applying binary logic systems for the analysis of real biomedical samples.
Finite difference equations describing salt and water movement in a model of the mammalian kidney have been solved numerically by an extension of the Newton-Raphson method used for the medullary counterflow system. The method permits both steady-state and transient solutions. It has been possible to simulate behavior of the whole kidney as a function of hydrostatic pressures in renal artery, vein, and pelvis; protein and other solute concentrations in arterial blood; and phenomenological equations describing transport of solute and water across nephron and capillary walls. With the model it has been possible to compute concentrations, flows, and hydrostatic pressures in the various nephron segments and in cortical and medullary capillaries and interstitium. In a general way, calculations on the model have met intuitive expectations. In addition, they have reemphasized the critical dependence of renal function on the hydraulic and solute permeabilities of glomerular, postglomerular, and medullary capillaries. These studies provide additional support for our thesis that the functional unit of the kidney is not the single nephron, but a nephrovascular unit consisting of a group of nephrons and their tightly coupled vasculature.
One-dimensional 1H nuclear magnetic resonance (1D 1H-NMR) has been used extensively as a metabolic profiling tool for investigating urine and other biological fluids. Under
ideal conditions, 1H-NMR peak intensities are directly proportional to metabolite concentrations and thus are useful for class prediction and
biomarker discovery. However, many biological, experimental and instrumental variables can affect absolute NMR peak intensities.
Normalizing or scaling data to minimize the influence of these variables is a critical step in producing robust, reproducible
analyses. Traditionally, analyses of biological fluids have relied on the total spectral area [constant sum (CS)] to normalize
individual intensities. This approach can introduce considerable inter-sample variance as changes in any individual metabolite
will affect the scaling of all of the observed intensities. To reduce normalization-related variance, we have developed a
histogram matching (HM) approach adapted from the field of image processing. We validate our approach using mixtures of synthetic
compounds that mimic a biological extract and apply the method to an analysis of urine from rats treated with ethionine. We
show that HM is a robust method for normalizing 1H-NMR data and propose it as an alternative to the traditional CS method.
Intestinal ischemia/reperfusion (I/R) injury initiates a systemic inflammatory response syndrome with a high associated mortality rate. Early diagnosis is essential for reducing surgical mortality, yet current clinical biomarkers are insufficient. Metabonomics is a novel strategy for studying intestinal I/R, which may be used as part of a systems approach for quantitatively analyzing the intestinal microbiome during gut injury. By deconvolving the mammalian-microbial symbiotic relationship systems biology thus has the potential for personalized risk stratification in patients exposed to intestinal I/R. This review describes the mechanism of intestinal I/R and explores the essential role of the intestinal microbiota in the initiation of systemic inflammatory response syndrome. Furthermore, it analyzes current and future approaches for elucidating the mechanism of this condition.
Resuscitation from hemorrhagic shock by infusion of isotonic (normal) saline (NS) is accompanied by a transient elevation in intracranial pressure (ICP), although cerebral edema, as measured by brain weights at 24 hours, is prevented by adequate volume resuscitation. The transient increase in ICP is not observed during hypertonic saline (HS) resuscitation. The effect of colloid resuscitation on ICP is unknown. Beagles were anesthetized, intubated, and ventilated, maintaining pCO2 between 30-45 torr. Femoral artery, pulmonary artery, and urethral catheters were positioned. ICP was measured with a subarachnoid bolt. Forty per cent of the dog's blood volume was shed and the shock state maintained for 1 hour. Resuscitation was done with shed blood and a volume of either NS (n = 5), 3% HS (n = 5), or 10% dextran-40 (D-40, n = 5) equal to the amount of shed blood. Intravascular volume was then maintained with NS. ICP fell from baseline values (4.7 +/- 3.13 mmHg) during the shock state and increased greatly during initial fluid resuscitation in NS and D-40 groups, to 16.0 +/- 5.83 mmHg and 16.2 +/- 2.68 mmHg, respectively. ICP returned to baseline values of 3.0 +/- 1.73 mmHg in the HS group with initial resuscitation and remained at baseline values throughout resuscitation. NS and D-40 ICP were greater than HS ICP at 1 hour (p less than .001) and 2 hours (p less than .05) after resuscitation. These results demonstrate that NS or colloid resuscitation from hemorrhagic shock elevates ICP and that HS prevents elevated ICP.
The creatinuria after musculoskeletal injuries was studied in 31 patients. In addition to creatine the urinary outputs of creatinine, total nitrogen, and 3-methylhistidine were studied. Plasma creatine, creatinine, and guanidinoacetate concentrations were measured in some patients. In rats the effect of bilateral hind-limb ischemia was observed on the urine outputs of creatine and creatinine, and on the creatine, protein, and DNA contents of the hind limbs and carcass. In man the creatinuria was positively related to the severity of the injury and this relationship was stronger than with the urinary output of either creatinine or total nitrogen. In the rat creatinuria was related to the duration of the limb ischemia. The mechanism of the creatinuria is not known but the experiments on rats showed part of the excess creatine to be derived from the damaged muscle and excreted shortly after the injury, and part from the undamaged muscle of the carcass which provided the delayed excretion. It is suggested that creatinuria could be used as an indicator of the post-traumatic 'flow' phase.
Whole body protein breakdown using 15N and skeletal muscle protein breakdown from urinary 3-methylhistidine were measured simultaneously in seven skeletal trauma and eight normal subjects on a standard hypocaloric, protein free diet. The trauma group had a 31% greater resting metabolic energy expenditure than controls. The control males lost 3.73 mumol/kg/day of 3-methylhistidine which suggested a protein breakdown rate of 0.89 g P/kg/day. The control females lost 2.46 mumol/kg/day of 3-methylhistidine or a breakdown rate of 0.58 g P/kg/day. These parameters were 187% greater for males and 163% greater for females in the trauma group. The measured whole body protein breakdown rates were 3.64 g P/kg/day for the control males and 2.69 for females. Skeletal trauma increased both by 73%. Skeletal trauma raised the muscle contribution to the whole body breakdown rate from 24.4 to 40.4% for men and from 21.6 to 33.0% for women. This disproportionate increase in muscle protein breakdown is consistent with muscle protein metabolism being most seriously affected by severe injury.
We show that an explicit method for solving hyperbolic partial differential equations can be applied to a model of a renal tubule to obtain both dynamic and steady-state solutions. Appropriate implementation of this method eliminates numerical instability arising from reversal of intratubular flow direction. To obtain second-order convergence in space and time, we employ the recently developed ENO (Essentially Non-Oscillatory) methodology. We present examples of computed flows and concentration profiles in representative model contexts. Finally, we indicate briefly how model tubules may be coupled to construct large-scale simulations of the renal counterflow system.
It is essential to identify patients at high risk of death and complications for future studies of interventions to decrease reperfusion injury.
We conducted an inception cohort study at a Level I trauma center to determine the rates and predictors of death, organ failure, and infection in trauma patients with systolic blood pressure < or = 90 mm Hg in the field or in the emergency department.
Among the 208 patients with hemorrhagic shock (blood pressure < or = 90 mm Hg), 31% died within 2 hours of emergency department arrival, 12% died between 2 and 24 hours, 11% died after 24 hours, and 46% survived. Among those who survived > or = 24 hours, 39% developed infection and 24% developed organ failure. Increasing volume of crystalloid in the first 24 hours was strongly associated with increased mortality (p = 0.00001).
Hemorrhage-induced hypotension in trauma patients is predictive of high mortality (54%) and morbidity. The requirement for large volumes of crystalloid was associated with increased mortality.
An increased diversity of therapeutic targets in the pharmaceutical industry in recent years has led to a greater diversity of toxicological effects. This, and the increased pace of drug discovery, leads to a need for new technologies for the rapid elucidation of toxicological mechanisms. As part of an evaluation of the utility of metabonomics in drug safety assessment, 1H NMR spectra were acquired on urine and liver tissue samples obtained from rats administered vehicle or a development compound (MrkA) previously shown to induce hepatotoxicity in several animal species. Multivariate statistical analysis of the urinary NMR data clearly discriminated drug-treated from control animals, due to a depletion in tricarboxylic acid cycle intermediates, and the appearance of medium chain dicarboxylic acids. High-resolution magic angle spinning NMR data acquired on liver samples exhibited elevated triglyceride levels that were correlated with changes in the urinary NMR data. Urinary dicarboxylic aciduria is associated with defective metabolism of fatty acids; subsequent in vitro experiments confirmed that MrkA impairs fatty acid metabolism. The successful application of metabonomics to characterize an otherwise ill-defined mechanism of drug-induced toxicity supports the practicality of this approach for resolving toxicity issues for drugs in discovery and development.
Reactive oxygen species (ROS) have been implicated in the pathogenesis of hemorrhagic shock. Ethyl pyruvate, a derivative of pyruvate and a proposed oxygen radical scavenger, is attractive as a possible resuscitation fluid. We investigated whether resuscitation with lactated Ringer's (LR) containing ethyl pyruvate (REP) had any hemodynamic or tissue energetic benefits compared with LR alone for hemorrhagic shock. Hemorrhagic shock was induced in splenectomized pigs via inferior vena cava cannula. After 90 min of shock, animals were resuscitated in a stepwise fashion with LR or REP (30 mg/kg/dose, given as 1.5 mg/mL in LR) at 20 cc/kg/step for four steps. Data collected during this experiment included physiologic and hemodynamic parameters, near-infrared reflectance spectroscopy measurements of tissue hemoglobin oxygen (StO(2)) of the stomach, liver, and hind limb, and nuclear magnetic resonance phosphorus spectra of the liver and hind limb at each time point. In both resuscitative groups, heart rate, and lactate and pyruvate values increased during shock and began to drop toward baseline values during resuscitation. Mean arterial pressure, oxygen delivery, and oxygen consumption decreased during shock and increased toward baseline levels during the resuscitative process. There were no significant changes in physiologic parameters between the LR- and REP-resuscitated animals. There was a significantly lower stomach StO(2) and hind limb cellular cytoplasmic pH during later resuscitative endpoints in REP-resuscitated animals. The clinical significance of these findings are unclear. There is no short-term hemodynamic or tissue energetic advantage to using REP as a resuscitation fluid when compared with LR. Long-term outcome studies are needed to further evaluate any potential benefits of use of REP in hemorrhagic shock.
Metabonomics involves the quantitation of the dynamic multivariate metabolic response of an organism to a pathological event or genetic modification [J.K. Nicholson, J.C. Lindon, E. Holmes, Xenobiotica 29 (1999) 1181-1189]. The analysis of these data involves the use of appropriate multivariate statistical methods; Principal Component Analysis (PCA) has been documented as a valuable pattern recognition technique for 1H NMR spectral data [J.T. Brindle, H. Antti, E. Holmes, G. Tranter, J.K. Nicholson, H.W. Bethell, S. Clarke, P.M. Schofield, E. McKilligin, D.E. Mosedale, D.J. Grainger, Nat. Med. 8 (2002) 1439-1444; B.C. Potts, A.J. Deese, G.J. Stevens, M.D. Reily, D.G. Robertson, J. Theiss, J. Pharm. Biomed. Anal. 26 (2001) 463-476; D.G. Robertson, M.D. Reily, R.E. Sigler, D.F. Wells, D.A. Paterson, T.K. Braden, Toxicol. Sci. 57 (2000) 326-337; L.C. Robosky, D.G. Robertson, J.D. Baker, S. Rane, M.D. Reily, Comb. Chem. High Throughput Screen. 5 (2002) 651-662]. Prior to PCA the raw data is typically processed through four steps; (1) baseline correction, (2) endogenous peak removal, (3) integration over spectral regions to reduce the number of variables, and (4) normalization. The effect of the size of spectral integration regions and normalization has not been well studied. The variability structure and classification accuracy on two distinctly different datasets are assessed via PCA and a leave-one-out cross-validation approach under two normalization approaches and an array of spectral integration regions. The first dataset consists of urine from 15 male Wistar-Hannover rats dosed with ANIT measured at five time points, mimicking drug-induced cholangiolitic hepatitis [D.G. Robertson, M.D. Reily, R.E. Sigler, D.F. Wells, D.A. Paterson, T.K. Braden, Toxicol. Sci. 57 (2000) 326-337; J.P. Shockcor, E. Holmes, Curr. Top. Med. Chem. 2 (2002) 35-51; N.J. Waters, E. Holmes, A. Williams, C.J. Waterfield, R.D. Farrant, J.K. Nicholson, Chem. Res. Toxicol. 14 (2001) 1401-1412]. The second data is serum samples from young male C57BL/6 mice subjected to instillation of pancreatic elastase producing emphysema type symptoms [C. Kuhn, S.Y. Yu, M. Chraplyvy, H.E. Linder, R.M. Senior, Lab. Invest. 34 (1976) 372-380; C. Kuhn, R.M. Senior, Lung 155 (1978) 185-197]. This study indicates that independent of the normalization method the classification accuracy achieved from metabonomic studies is not highly sensitive to the size of the spectral integration region. Additionally, both datasets scaled to mean zero and unity variance (auto-scaled) have higher variability within classification accuracy over spectral integration window widths than data scaled to the total intensity of the spectrum. Of the top 10 latent variables for the ANIT dataset the auto-scale normalization has standard deviations larger than the total-scale in seven cases. In the case of the elastase all standard deviations are larger for the auto-scaling.
The success of any given kidney transplant is closely tied to the ability to monitor patients and responsively change their medications. Transplant monitoring is still, however, dependent on relatively old technologies: serum creatinine levels, urine output, blood pressure, blood glucose and histopathology of biopsy samples. These older technologies do not offer sufficient specificity, sensitivity, or accuracy to allow appropriate and timely interventions. Using the tools of genomics, proteomics and metabolomics new biomarkers are being found that may greatly improve transplant monitoring and significantly enhance graft survival. This review describes the basic principles of metabolomics and summarizes a number of recent developments in the use of metabolite biomarkers and metabolomics to monitor kidney transplants.
Changes in the concentration profiles of a number of small molecule metabolites found in either blood or urine can be used to localize organ damage, identify organs at risk of rejection, assess organs suffering from ischemia-repurfusion injury or identify organs that have been damaged by immunosuppressive drugs.
The application of metabolomics to kidney transplant monitoring is still very much in its infancy. Nevertheless, there are a number of easily measured metabolites in both urine and serum that can provide reliable indications of organ function, organ injury, and immunosuppressive drug toxicity. As the field matures, metabolomics may eventually lead to the development of rapid, inexpensive and noninvasive approaches to assist clinicians in monitoring kidney transplants.
Hypoxia is the major cause of necrotic cell death in myocardial infarction. Cellular energy supply and demand under hypoxic conditions is regulated by many interacting signaling and transcriptional networks, which complicates studies on individual proteins and pathways. We apply an integrated systems approach to understand the metabolic and functional response to hypoxia in muscle cells of the fruit fly Drosophila melanogaster. In addition to its utility as a hypoxia-tolerant model organism, Drosophila also offers advantages due to its small size, fecundity, and short life cycle. These traits, along with a large library of single-gene mutations, motivated us to develop new, computer-automated technology for gathering in vivo measurements of heart function under hypoxia for a large number of mutant strains. Phenotype data can be integrated with in silico cellular networks, metabolomic data, and microarrays to form qualitative and quantitative network models for prediction and hypothesis generation. Here we present a framework for a systems approach to hypoxia in the cardiac myocyte, starting from nuclear magnetic resonance (NMR) metabolomics, a constraint-based metabolic model, and phenotypic profiles.
The defect in energy production in an organism during shock states may be related to the impairment of mitochondrial respiration early in shock. The aim of this study was to investigate the timing and degree of cellular energetic changes during hemorrhagic shock in real time. Instrumented, splenectomized swine were randomized to undergo hemorrhagic shock, induced by a 35% blood volume bleed, for 90 min with (n = 10) or without (n = 9) subsequent resuscitation. Resuscitated animals received shed blood in two increments followed by two normal saline boluses (20 mL/kg/bolus). Throughout experimentation, tissue phosphoenergetics of liver and skeletal muscle were monitored using 31P nuclear magnetic resonance (NMR) spectroscopy via NMR coils on the liver and hindlimb. Near-infrared spectroscopy probes were used to measure liver, stomach, and skeletal muscle oxyhemoglobin saturation (StO2). Hemorrhagic shock induced an increase in phosphomonoesters in skeletal muscle (baseline: 7.09%, 90 min: 9.94% (P < 0.05); expressed as percent total phosphorus). This increase resolved in animals receiving resuscitation (n = 10) but remained elevated in those in unresuscitated shock (n = 9). Inorganic phosphate levels increased and betaATP levels decreased significantly in the liver of animals in shock as compared with baseline. StO2 in skeletal muscle, stomach, and liver correlated with whole organism oxygen delivery (r2 = 0.356, 0.368, and 0.432, respectively). We conclude that hemorrhagic shock induces early elevation of phosphomonoesters in skeletal muscle, which correlates with the severity of shock. This implies an early transition to anaerobic glycolysis during hemorrhagic shock, which may be indicative of early mitochondrial dysfunction.