Erik J. Saude

Metabolomic profiling of asthma: diagnostic utility of urine nuclear magnetic resonance spectroscopy.

BACKGROUND The ability to diagnose and monitor asthma on the basis of noninvasive measurements of airway cellular dysfunction is difficult in the typical clinical setting. OBJECTIVE Metabolomics is the study of molecules created by cellular metabolic pathways. We hypothesized that the metabolic activity of children with asthma would differ from healthy children without asthma. Furthermore, children having an asthma exacerbation would be different compared with children with stable asthma in outpatient clinics. Finally, we hypothesized that (1)H-nuclear magnetic resonance (NMR) would measure such differences using urine samples, one of the least invasive forms of biofluid sampling. METHODS Children (135 total, ages 4-16 years) were enrolled, having met the criteria of healthy controls (C), stable asthma in the outpatient clinic (AO), or unstable asthma in the emergency department (AED). Partial least squares discriminant analysis was performed on the NMR data to create models of separation (70 metabolites were measured/urine sample). Some NMR data were withheld from modeling to be run blindly to determine possible diagnostic accuracy. RESULTS On the basis of the model of AO versus C, 31 of 33 AO samples were correctly diagnosed with asthma (94% accuracy). Only 1 of 20 C samples was incorrectly labeled as asthma (5% misclassification). On the basis of the AO versus AED model, 31 of the 33 AO samples were correctly diagnosed as outpatient asthma (94% accurate). CONCLUSION This is the first report suggesting that (1)H-NMR analysis of human urine samples has the potential to be a useful clinical tool for physicians treating asthma.

Optimization of NMR analysis of biological fluids for quantitative accuracy

With the rising interest in the use of nuclear magnetic resonance (NMR) for the study of biological fluids such as urine and serum for metabonomic or diagnostic purposes, new challenges have arisen concerning the efficacy of NMR data acquisition and analysis. In particular the quantification of sample constituents such as metabolites is of great importance. This study compares five one-dimensional proton NMR pulse sequences using synthetic urine samples to determine appropriate acquisition parameters for reasonable sample throughput and accuracy. Each pulse sequence has its own advantages and limitations with respect to solvent suppression, stable baseline, exchangeable protons, and quantization of resonances near the residual water peak. Hardware issues such as low-pass filters, unique to each spectrometer, also impact quantitation accuracy. Metabolite concentrations were determined using integration referenced to an added internal standard, and using the Chenomx NMR Suite software package. Since nuclei in different metabolites and the internal standard all have different longitudinal relaxation rates (T1) we included a mathematical correction factor for quantitation.

Pneumococcal Pneumonia: Potential for Diagnosis through a Urinary Metabolic Profile

Pneumonia, an infection of the lower respiratory tract, is caused by any of a number of different microbial organisms including bacteria, viruses, fungi, and parasites. Community-acquired pneumonia (CAP) causes a significant number of deaths worldwide, and is the sixth leading cause of death in the United States. However, the pathogen(s) responsible for CAP can be difficult to identify, often leading to delays in appropriate antimicrobial therapies. In the present study, we use nuclear magnetic resonance spectroscopy to quantitatively measure the profile of metabolites excreted in the urine of patients with pneumonia caused by Streptococcus pneumoniae and other microbes. We found that the urinary metabolomic profile for pneumococcal pneumonia was significantly different from the profiles for viral and other bacterial forms of pneumonia. These data demonstrate that urinary metabolomic profiles may be useful for the effective diagnosis of CAP.

Metabolomic biomarkers in a model of asthma exacerbation: urine nuclear magnetic resonance.

RATIONALE Airway obstruction in patients with asthma is associated with airway dysfunction and inflammation. Objective measurements including sputum analysis can guide therapy, but this is often not possible in typical clinical settings. Metabolomics is the study of molecules generated by metabolic pathways. We hypothesize that airway dysfunction and inflammation in an animal model of asthma would produce unique patterns of urine metabolites measured by multivariate statistical analysis of high-resolution proton nuclear magnetic resonance ((1)H NMR) spectroscopy data. OBJECTIVES To develop a noninvasive means of monitoring asthma status by metabolomics and urine sampling. METHODS Five groups of guinea pigs were studied: control, control treated with dexamethasone, sensitized (ovalbumin, administered intraperitoneally), sensitized and challenged (ovalbumin, administered intraperitoneally, plus ovalbumin aerosol), and sensitized-challenged with dexamethasone. Airway hyperreactivity (AHR) to histamine (administered intravenously) and inflammation were measured. Multivariate statistical analysis of NMR spectra based on a library of known urine metabolites was performed by partial least-squares discriminant analysis. In addition, the raw NMR spectra exported as xy-trace data underwent linear discriminant analysis. MEASUREMENTS AND MAIN RESULTS Challenged guinea pigs developed AHR and increased inflammation compared with sensitized or control animals. Dexamethasone significantly improved AHR. Using concentration differences in metabolites, partial least-squares discriminant analysis could discriminate challenged animals with 90% accuracy. Using only three or four regions of the NMR spectra, linear discriminant analysis-based classification demonstrated 80-90% separation of the animal groups. CONCLUSIONS Urine metabolites correlate with airway dysfunction in an asthma model. Urine NMR analysis is a promising, noninvasive technique for monitoring asthma in humans.

NMR Analysis of Neutrophil Activation in Sputum Samples From Patients With Cystic Fibrosis

Disorders of the respiratory system, such as cystic fibrosis (CF), involve the infiltration and activation of airway inflammatory cells, including neutrophils. This leads to the secretion of peroxidases, which react further with substrates in solution to produce oxidative metabolites, such as 3‐chlorotyrosine. Elevated levels of modified tyrosine residues in the airways of patients with CF may be detectable by nuclear magnetic resonance (NMR) in correlation with inflammatory cell influx. In this study, high‐resolution (500 MHz) 1H NMR was used to analyze the production of modified tyrosine residues resulting from in vitro stimulation of peripheral blood eosinophils and neutrophils, as well as in sputum samples from control subjects and patients with CF. Following in vitro stimulation, purified peripheral blood neutrophils generated 3‐chlorotyrosine, while eosinophils produced predominantly 3‐bromotyrosine and 3,5‐dibromotyrosine. Chlorinated and brominated tyrosine residues were detected in sputum samples from patients with CF (N = 7), but were not detected in the control group (N = 9). Neutrophil counts in CF sputum correlated strongly with the presence of 3‐chlorotyrosine (r2 = 0.869). Our findings indicate that neutrophil and eosinophil activation in CF is detectable by NMR. NMR may be a useful tool for the detection of biological markers of inflammatory processes in patient airways. Magn Reson Med 52:807–814, 2004.

Monitoring asthma status.

PurposeAsthma is a chronic disorder of the airways involving inflammation and airway hyper-reactivity. Clinical diagnosis and monitoring of asthma must incorporate the immunological, biochemical, and histological changes of a chronic disorder, while recognizing acute phenotypic changes in order to optimally tailor therapeutics to each individual. Recent findingsArticles published within the previous 18 months are summarized in this article in order to present an up to date review of the latest findings regarding the monitoring of asthma. The articles encompass a wide array of specialties from basic research and histology to clinical medicine as well as community medicine and nursing. SummaryExciting new advancements in the monitoring of asthma continue to unfold. Potentially new diagnostic and monitoring tools are highlighted in this study. Continued investigations may enable a select few methodologies to reach clinical utility in the ongoing monitoring and treatment of patients with asthma.

Back pain in the emergency department: Pathological fracture following spinal manipulation

Back pain is one of the most common presentations to the emergency department. Though case reports of patients presenting with increased back pain following chiropractic spinal manipulations are rare, we have identified a case rarely reported in the literature where a potential injury from chiropractic manipulation resulted in a diagnosis of multiple myeloma. We have reported a previously healthy 66-year-old male who presented with persistent lower back pain over 4 weeks. An initial evaluation with thoracolumbar radiographs revealed no significant findings. Following initial presentation to the family physician, the patient underwent three treatments of spinal manipulation from his local chiropractor, which resulted in worsening lower back pain. A re-examination and new radiographs in the hospital revealed multiple compression fractures and an underlying diagnosis of multiple myeloma. We have explored current literature examining the prevalence of lower back pain, as well as the incidence of spinal fracture following chiropractic manipulation, and have highlighted a potential complication from chiropractic manipulation in a patient with an undiagnosed underlying neoplastic disorder.

Virus memory induces airway hyperreactivity through eosinophil activation

Objective/purpose Asthma is the most common chronic respiratory disease in children. Asthma exacerbation occurs when the airways acutely become obstructed, usually the result of airway inflammation. The Inflammation is caused by a unique mix of cells, and includes eosinophils. The majority of asthma exacerbations occur after a viral infection such as a common cold. Why asthmatic children develop such severe reactions to viruses is unclear. Our previous work suggests asthmatic patients develop severe airway obstruction because they have too many eosinophils in their airways before virus infection. The virus triggers these eosinophils to release harmful mediators and cause airway damage. We believe that in humans, it may be the mere presence of virus antigen that stimulates memory cells to activate the eosinophils. We hypothesize that memory T cell proliferation and eosinophil activation will occur in response to any airway virus for which immune memory exists, and that removal of the eosinophils will prevent airway hyperreactivity (AHR). In addition, we believe that this model is representative of virus-induced asthma exacerbation. As part of our project to develop non-invasive diagnostics using the metabolomic profile of urine through Nuclear Magnetic Resonance (NMR) spectroscopy, we are saving the urine samples from these animals. We hypothesize that there will be relevant differences between the urine profiles of each animal group, which will be applicable to humans.

FEATURE SELECTION AND CLASSIFICATION OF METABOLOMIC DATA USING SUPPORT VECTOR MACHINES

Abstract Over the past few years there has been an explosion of biological data available for exploratory analysis. The main task of data analysis is to extract meaningful information in a way that facilitates the understanding of the complex biological processes. In order to do this, algorithms and techniques have to be developed that can be trained to learn rules and form patterns from the available data sets and then apply these rules to analyse new data. In computing science terminology this is known as machine learning. In this paper, the applicability of one such machine learning technique, namely ‘support vector machines’ to analyze and classify metabolomic data is explored. The paper also explores some of the feature selection algorithms which help determine important biomarkers or metabolites in data sets.