Potential of magnetic resonance spectroscopy in assessing the effect of fatty acids on inflammatory bowel disease in an animal model.

People with inflammatory bowel disease (IBD) are at risk for developing colorectal cancer, and this risk increases at a rate of 1% per year after 8-10 years of having the disease. Saturated and omega-6 polyunsaturated fatty acids (PUFAs) have been implicated in its causation. Conversely, omega-3 PUFAs may have the potential to confer therapeutic benefit. Since proton magnetic resonance spectroscopy ((1)H MRS) combined with pattern recognition methods could be a valuable adjunct to histology, the objective of this study was to analyze the potential of (1)H MRS in assessing the effect of dietary fatty acids on colonic inflammation. Forty male Sprague-Dawley rats were administered one of the following dietary regimens for 2 weeks: low-fat corn oil (omega-6), high-fat corn oil (omega-6), high-fat flaxseed oil (omega-3) or high-fat beef tallow (saturated fatty acids). Half of the animals were fed 2% carrageenan to induce colonic inflammation similar to IBD. (1)H MRS and histology were performed on ex vivo colonic samples, and the (1)H MR spectra were analyzed using a statistical classification strategy (SCS). The histological and/or MRS studies revealed that different dietary fatty acids modulate colonic inflammation differently, with high-fat corn oil being the most inflammatory and high-fat flaxseed oil the least inflammatory. (1)H MRS is capable of identifying the biochemical changes in the colonic tissue as a result of inflammation, and when combined with SCS, this technique accurately differentiated the inflamed colonic mucosa based on the severity of the inflammation. This indicates that MRS could serve as a valuable adjunct to histology in accurately assessing colonic inflammation. Our data also suggest that both the type and the amount of fatty acids in the diet are critical in modulating IBD.

Rapid etiological classification of meningitis by NMR spectroscopy based on metabolite profiles and host response.

Bacterial meningitis is an acute disease with high mortality that is reduced by early treatment. Identification of the causative microorganism by culture is sensitive but slow. Large volumes of cerebrospinal fluid (CSF) are required to maximise sensitivity and establish a provisional diagnosis. We have utilised nuclear magnetic resonance (NMR) spectroscopy to rapidly characterise the biochemical profile of CSF from normal rats and animals with pneumococcal or cryptococcal meningitis. Use of a miniaturised capillary NMR system overcame limitations caused by small CSF volumes and low metabolite concentrations. The analysis of the complex NMR spectroscopic data by a supervised statistical classification strategy included major, minor and unidentified metabolites. Reproducible spectral profiles were generated within less than three minutes, and revealed differences in the relative amounts of glucose, lactate, citrate, amino acid residues, acetate and polyols in the three groups. Contributions from microbial metabolism and inflammatory cells were evident. The computerised statistical classification strategy is based on both major metabolites and minor, partially unidentified metabolites. This data analysis proved highly specific for diagnosis (100% specificity in the final validation set), provided those with visible blood contamination were excluded from analysis; 6-8% of samples were classified as indeterminate. This proof of principle study suggests that a rapid etiologic diagnosis of meningitis is possible without prior culture. The method can be fully automated and avoids delays due to processing and selective identification of specific pathogens that are inherent in DNA-based techniques.

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.

Detection of inflammatory bowel disease by proton magnetic resonance spectroscopy (1H MRS) using an animal model.

BACKGROUND: The aim of this study was to analyze the potential of proton magnetic resonance spectroscopy (1H MRS) in diagnosing early inflammatory bowel disease (IBD). METHODS: Thirty male Sprague Dawley rats were fed 2% carrageenan in their diet for either 1 or 2 weeks. 1H MRS was performed ex-vivo on colonic mucosal samples (n = 123) and the spectra were analyzed by a multivariate method of analysis. The results of the multivariate analysis were correlated with histological analysis performed using H & E stain for the presence of inflammation in the samples from each group. RESULTS: Multivariate analysis classified the samples in their respective groups with an accuracy of 82%. Our region selection algorithm identified four regions in the spectra as being discriminatory. The metabolites assigned to these regions include creatine, phosphatidylcholine, the -CH2HC= group in fatty acyl chain, and the glycerol backbone of lipids. The differences in concentration of these metabolites in each group offer insight into the biochemical changes occurring during IBD and confer diagnostic potential to 1H MRS as a tool to study colonic inflammation in conjunction with biopsy. CONCLUSION: 1H MRS is a sensitive tool to detect early colonic inflammation in an animal model of IBD.

Magnetic resonance spectroscopy detects biochemical changes in the brain associated with chronic low back pain: a preliminary report.

Magnetic resonance (MR) spectroscopy is a noninvasive technique that can be used to detect and measure the concentration of metabolites and neurotransmitters in the brain and other organs. We used in vivo (1)H MR spectroscopy in subjects with low back pain compared with control subjects to detect alterations in biochemistry in three brain regions associated with pain processing. A pattern recognition approach was used to determine whether it was possible to discriminate accurately subjects with low back pain from control subjects based on MR spectroscopy. MR spectra were obtained from the prefrontal cortex, anterior cingulate cortex, and thalamus of 32 subjects with low back pain and 33 control subjects without pain. Spectra were analyzed and compared between groups using a pattern recognition method (Statistical Classification Strategy). Using this approach, it was possible to discriminate between subjects with low back pain and control subjects with accuracies of 100%, 99%, and 97% using spectra obtained from the anterior cingulate cortex, thalamus, and prefrontal cortex, respectively. These results demonstrate that MR spectroscopy, in combination with an appropriate pattern recognition approach, is able to detect brain biochemical changes associated with chronic pain with a high degree of accuracy.

Melanoma metastases in regional lymph nodes are accurately detected by proton magnetic resonance spectroscopy of fine-needle aspirate biopsy samples.

BACKGROUND: Nonsurgical assessment of sentinel nodes (SNs) would offer advantages over surgical SN excision by reducing morbidity and costs. Proton magnetic resonance spectroscopy (MRS) of fine-needle aspirate biopsy (FNAB) specimens identifies melanoma lymph node metastases. This study was undertaken to determine the accuracy of the MRS method and thereby establish a basis for the future development of a nonsurgical technique for assessing SNs. METHODS: FNAB samples were obtained from 118 biopsy specimens from 77 patients during SN biopsy and regional lymphadenectomy. The specimens were histologically evaluated and correlated with MRS data. Histopathologic analysis established that 56 specimens contained metastatic melanoma and that 62 specimens were benign. A linear discriminant analysis-based classifier was developed for benign tissues and metastases. RESULTS: The presence of metastatic melanoma in lymph nodes was predicted with a sensitivity of 92.9%, a specificity of 90.3%, and an accuracy of 91.5% in a primary data set. In a second data set that used FNAB samples separate from the original tissue samples, melanoma metastases were predicted with a sensitivity of 87.5%, a specificity of 90.3%, and an accuracy of 89.1%, thus supporting the reproducibility of the method. CONCLUSIONS: Proton MRS of FNAB samples may provide a robust and accurate diagnosis of metastatic disease in the regional lymph nodes of melanoma patients. These data indicate the potential for SN staging of melanoma without surgical biopsy and histopathological evaluation.

A rapid screening test to distinguish between Candida albicans and Candida dubliniensis using NMR spectroscopy.

Nuclear magnetic resonance (NMR) spectroscopy combined with a statistical classification strategy (SCS) successfully distinguished between Candida albicans and Candida dubliniensis. 96% of the isolates from an independent test set were identified correctly. This proves that this rapid approach is a valuable method for the identification and chemotaxonomic characterisation of closely related taxa. Most discriminatory regions were correlated with metabolite profiles, indicating biochemical differences between the two species.

Diagnosis of cerebral cryptococcoma using a computerized analysis of 1H NMR spectra in an animal model.

Viable cryptococci load in biopsy material from an animal model of cerebral cryptococcoma were correlated with 1H NMR spectra and metabolite profiles. A statistical classification strategy was applied to distinguish among high-resolution 1H NMR spectra acquired from cryptococcomas, glioblastomas, and normal brain tissue. The overall classification accuracy was 100% when a genetic-algorithm-based optimal region selection preceded the development of linear discriminant analysis-based classifiers. The method remained robust despite differences in the microbial load of the cryptococcoma group when harvested at different time points. These results indicate the feasibility of the method for diagnosis without isolation of the pathogenic microorganism and its potential for in vivo diagnosis based on computerized analysis of magnetic resonance spectra.

Identification of Staphylococcus aureus brain abscesses: rat and human studies with 1H MR spectroscopy.

PURPOSE: To determine the feasibility of a statistical classification strategy (SCS) and the identity of metabolites of bacterial and host origins that potentially contributed to the most discriminatory regions of magnetic resonance (MR) spectra from Staphylococcus aureus abscesses of biopsy material from controls, gliomas, and staphylococcal abscesses. MATERIALS AND METHODS: Human and animal study received ethics committee approval, and informed patient consent was obtained. A rat model of S aureus brain abscess was developed. Histologic and microbiologic examination was performed to assess abscess development 3-4, 6-8, and 10-15 days after initiation. Metabolite profiles in pus (n = 62) and controls (n = 37) were characterized with ex vivo MR spectroscopy and compared with data from rat gliomas (n = 27). SCS, optimal region selection, and development of pairwise classifiers allowed MR spectra of abscesses (n = 42, day 6-8) to be distinguished from those of glioblastoma multiforme and controls. MR spectroscopy profiles of pus from animal abscesses were compared with in vivo MR spectra from patients with staphylococcal brain abscesses (n = 7, aged 6-67 years) and ex vivo pus MR spectra from patients with S aureus abscesses. RESULTS: Histologically confirmed abscesses were present 6-8 days after stereotactic injection of S aureus in 42 of 47 rats (89%). MR spectra of abscesses and glioblastoma multiforme in the animal model were similar. Typical metabolites of abscesses due to anaerobe bacteria (acetate, succinate, amino acids) were not detectable in S aureus abscesses in rats or humans. MR spectroscopic findings from controls, abscesses, and gliomas were distinguished by means of SCS with an accuracy of 99%. Analysis of the most discriminatory regions with two-dimensional correlation spectra indicated that glutamine and/or glutamate and aspartate potentially contributed to successful classification. CONCLUSION: S aureus is detectable in abscesses with a non-culture-based method in an animal model.

Determination of grade and receptor status from the primary breast lesion by magnetic resonance spectroscopy.

Magnetic resonance spectra (MRS) from fine needle aspiration biopsies (FNAB) from primary breast lesions were analysed using a pattern recognition method, Statistical Classification Strategy, to assess tumor grade and oestrogen receptor (ER) and progesterone receptor (PgR) status. Grade 1 and 2 breast cancers were separated from grade 3 cancers with a sensitivity and specificity of 96% and 95%, respectively. The ER status was predicted with a sensitivity of 91% and a specificity of 90%, and the PgR status with a sensitivity of 91% and a specificity of 86%. These classifiers provide rapid and reliable, computerized information and may offer an objective method for determining these prognostic indicators simultaneously with the diagnosis of primary pathology and lymph node involvement.

Molecular classification of scrapie strains in mice using gene expression profiling.

Transmissible spongiform encephalopathy strains demonstrate specific prion characteristics, each with specific incubation times, and strain-specific patterns of deposition of the misfolded isoform of prion, PrPSc, in the brains of infected individuals. Different biochemical properties, including glycosylation profiles and the degree of proteinase resistance, have been shown to be strain-specific. However, no relationship between these properties and the phenotypic differences in the subsequent diseases has as yet been determined. Here we explore the utility of gene expression profiles to identify differences in the host response to different strains of prion agent. We identify 114 genes that exhibit significantly different levels of expression in mice infected with three strains of scrapie. These genes represent a pool of genes involved in a strain-specific response to prion disease. We have identified the most discriminatory genes from this list utilizing a wrapper-based feature selection algorithm with external cross-validation.

Rapid identification of Candida species by using nuclear magnetic resonance spectroscopy and a statistical classification strategy.

Nuclear magnetic resonance (NMR) spectra were acquired from suspensions of clinically important yeast species of the genus Candida to characterize the relationship between metabolite profiles and species identification. Major metabolites were identified by using two-dimensional correlation NMR spectroscopy. One-dimensional proton NMR spectra were analyzed by using a staged statistical classification strategy. Analysis of NMR spectra from 442 isolates of Candida albicans, C. glabrata, C. krusei, C. parapsilosis, and C. tropicalis resulted in rapid, accurate identification when compared with conventional and DNA-based identification. Spectral regions used for the classification of the five yeast species revealed species-specific differences in relative amounts of lipids, trehalose, polyols, and other metabolites. Isolates of C. parapsilosis and C. glabrata with unusual PCR fingerprinting patterns also generated atypical NMR spectra, suggesting the possibility of intraspecies discontinuity. We conclude that NMR spectroscopy combined with a statistical classification strategy is a rapid, nondestructive, and potentially valuable method for identification and chemotaxonomic characterization that may be broadly applicable to fungi and other microorganisms.

Pathology of Barrett's esophagus by proton magnetic resonance spectroscopy and a statistical classification strategy.

BACKGROUND: Barrett's esophagus is thought to be a precursor of adenocarcinoma. The incidence of adenocarcinoma of the lower esophagus in the Western world is rising and accounts for more than 40% of esophageal carcinomas in males. It is not possible to identify which Barrett's patients are at high risk of developing malignancy. Here we applied a statistical classification strategy to the analysis of magnetic resonance spectroscopy and histopathological data from esophageal biopsies to ascertain whether this risk could be identified in Barrett's patients. METHODS: Tissue specimens from 72 patients (29 noncancer-bearing and 43 cancer-bearing) were analyzed by one-dimensional proton magnetic resonance spectroscopy at 8.5 Tesla. Diagnostic correlation was performed between the magnetic resonance spectra and histopathology. The magnetic resonance magnitude spectra were preprocessed, followed by identification of optimal spectral regions, and were then classified by cross-validated linear discriminant analysis of rank orders of the first derivative of magnetic resonance spectra. RESULTS: Magnetic resonance spectroscopy combined with a statistical classification strategy analysis distinguished normal esophagus from adenocarcinoma and Barrett's epithelium with an accuracy of 100%. Barrett's epithelium and adenocarcinoma were distinguished with an accuracy of 98.6% but only when 4 of the Barrett's specimens and 7 of the carcinoma specimens, determined to be "fuzzy" (ie, unable to be accurately assigned to either class) were withdrawn. The 7 cancer and 4 Barrett's specimens, determined to be "fuzzy" using the Barrett's versus cancer (B versus C) classifier, were submitted to the other two classifiers (Barrett's versus normal [B versus N] and normal versus cancer [N versus C], respectively). The 4 Barrett's specimens were assigned to Barrett's by the N versus B classifier and to normal (n = 2) or cancer (n = 2) classes by the N versus C classifier. The 7 cancer specimens were crisply assigned to the cancer class (N versus C), or for the B versus N classifier, to the Barrett's class (ie, more similar to Barrett's than to normal tissue). Visual inspection of the spectra from histologically identified Barrett's epithelium showed a gradation from normal to carcinoma. CONCLUSIONS: Proton magnetic resonance spectroscopy of esophageal biopsies combined with a statistical classification strategy data analysis provides a robust diagnosis with a high degree of accuracy for discriminating normal epithelium from esophageal adenocarcinoma and Barrett's esophagus. Different spectral categories of Barrett's epithelium were identified both by visual inspection and by statistical classification strategy, possibly reflecting the risk of future malignant transformation.

Pathology of hepatocellular carcinoma and its precursors using proton magnetic resonance spectroscopy and a statistical classification strategy.

AIM: Apply the statistical classification strategy (SCS) to magnetic resonance spectroscopy (MRS) data from liver biopsies and test its potential to discriminate between normal liver, cirrhotic nodules and nodules of hepatocellular carcinoma with a high degree of accuracy. METHODS: Liver tissue specimens from 54 patients undergoing either partial (hemi) or total hepatectomy were analysed by one-dimensional proton MRS at 8.5 Tesla. Histologically, these specimens were confirmed as normal (n=31), cirrhotic (n=59), and hepatocellular carcinoma (HCC, n=32). Diagnostic correlation was performed between the MR spectra and histopathology. An SCS was applied consisting of pre-processing MR magnitude spectra to identify spectral regions of maximal discriminatory value, and cross-validated linear discriminant analysis. RESULTS: SCS applied to MRS data distinguished normal liver tissue from HCC with an accuracy of 100%. Normal liver tissue was distinguished from cirrhotic liver with an accuracy of 92% and cirrhotic liver was distinguished from HCC with an accuracy of 98%. CONCLUSIONS: SCS applied to proton MRS of liver biopsies provides a robust method to distinguish, with a high degree of accuracy, HCC from both cirrhotic and normal liver.