Classification of brain tumor extracts by high resolution ¹H MRS using partial least squares discriminant analysis

High resolution proton nuclear magnetic resonance spectroscopy (¹H MRS) can be used to detect biochemical changes in vitro caused by distinct pathologies. It can reveal distinct metabolic profiles of brain tumors although the accurate analysis and classification of different spectra remains a challenge. In this study, the pattern recognition method partial least squares discriminant analysis (PLS-DA) was used to classify 11.7 T ¹H MRS spectra of brain tissue extracts from patients with brain tumors into four classes (high-grade neuroglial, low-grade neuroglial, non-neuroglial, and metastasis) and a group of control brain tissue. PLS-DA revealed 9 metabolites as the most important in group differentiation: γ-aminobutyric acid, acetoacetate, alanine, creatine, glutamate/glutamine, glycine, myo-inositol, N-acetylaspartate, and choline compounds. Leave-one-out cross-validation showed that PLS-DA was efficient in group characterization. The metabolic patterns detected can be explained on the basis of previous multimodal studies of tumor metabolism and are consistent with neoplastic cell abnormalities possibly related to high turnover, resistance to apoptosis, osmotic stress and tumor tendency to use alternative energetic pathways such as glycolysis and ketogenesis.

Serum metabolic profiling of human gastric cancer based on gas chromatography/mass spectrometry

Research on molecular mechanisms of carcinogenesis plays an important role in diagnosing and treating gastric cancer. Metabolic profiling may offer the opportunity to understand the molecular mechanism of carcinogenesis and help to non-invasively identify the potential biomarkers for the early diagnosis of human gastric cancer. The aims of this study were to explore the underlying metabolic mechanisms of gastric cancer and to identify biomarkers associated with morbidity. Gas chromatography/mass spectrometry (GC/MS) was used to analyze the serum metabolites of 30 Chinese gastric cancer patients and 30 healthy controls. Diagnostic models for gastric cancer were constructed using orthogonal partial least squares discriminant analysis (OPLS-DA). Acquired metabolomic data were analyzed by the nonparametric Wilcoxon test to find serum metabolic biomarkers for gastric cancer. The OPLS-DA model showed adequate discrimination between cancer and non-cancer cohorts while the model failed to discriminate different pathological stages (I-IV) of gastric cancer patients. A total of 44 endogenous metabolites such as amino acids, organic acids, carbohydrates, fatty acids, and steroids were detected, of which 18 differential metabolites were identified with significant differences. A total of 13 variables were obtained for their greatest contribution in the discriminating OPLS-DA model [variable importance in the projection (VIP) value >1.0], among which 11 metabolites were identified using both VIP values (VIP >1) and the Wilcoxon test. These metabolites potentially revealed perturbations of glycolysis and of amino acid, fatty acid, cholesterol, and nucleotide metabolism of gastric cancer patients. These results suggest that gastric cancer serum metabolic profiling has great potential in detecting this disease and helping to understand its metabolic mechanisms.

Analysis of acylcarnitine profiles in umbilical cord blood and during the early neonatal period by electrospray ionization tandem mass spectrometry

Acylcarnitine profiling by electrospray ionization tandem mass spectrometry (ESI-MS/MS) is a potent tool for the diagnosis and screening of fatty acid oxidation and organic acid disorders. Few studies have analyzed free carnitine and acylcarnitines in dried blood spots (DBS) of umbilical cord blood (CB) and the postnatal changes in the concentrations of these analytes. We have investigated these metabolites in healthy exclusively breastfed neonates and examined possible effects of birth weight and gestational age. DBS of CB were collected from 162 adequate for gestational age neonates. Paired DBS of heel-prick blood were collected 4-8 days after birth from 106 of these neonates, the majority exclusively breastfed. Methanol extracts of DBS with deuterium-labeled internal standards were derivatized before analysis by ESI-MS/MS. Most of the analytes were measured using a full-scan method. The levels of the major long-chain acylcarnitines, palmitoylcarnitine, stearoylcarnitine, and oleoylcarnitine, increased by 27, 12, and 109%, respectively, in the first week of life. Free carnitine and acetylcarnitine had a modest increase: 8 and 11%, respectively. Propionylcarnitine presented a different behavior, decreasing 9% during the period. The correlations between birth weight or gestational age and the concentrations of the analytes in DBS were weak (r £ 0.20) or nonsignificant. Adaptation to breast milk as the sole source of nutrients can explain the increase of these metabolites along the early neonatal period. Acylcarnitine profiling in CB should have a role in the early detection of metabolic disorders in high-risk neonates.

Thalamic metabolic abnormalities in patients with Huntington's disease measured by magnetic resonance spectroscopy

Huntington's disease (HD) is a neurologic disorder that is not completely understood; its fundamental physiological mechanisms and chemical effects remain somewhat unclear. Among these uncertainties, we can highlight information about the concentrations of brain metabolites, which have been widely discussed. Concentration differences in affected, compared to healthy, individuals could lead to the development of useful tools for evaluating the progression of disease, or to the advance of investigations of different/alternative treatments. The aim of this study was to compare the thalamic concentration of metabolites in HD patients and healthy individuals using magnetic resonance spectroscopy. We used a 2.0-Tesla magnetic field, repetition time of 1500 ms, and echo time of 135 ms. Spectra from 40 adult HD patients and 26 control subjects were compared. Quantitative analysis was performed using the LCModel method. There were statistically significant differences between HD patients and controls in the concentrations ofN-acetylaspartate+N-acetylaspartylglutamate (NAA+NAAG; t-test, P<0.001), and glycerophosphocholine+phosphocholine (GPC+PCh;t-test, P=0.001) relative to creatine+phosphocreatine (Cr+PCr). The NAA+NAAG/Cr+PCr ratio was decreased by 9% and GPC+PCh/Cr+PCr increased by 17% in patients compared with controls. There were no correlations between the concentration ratios and clinical features. Although these results could be caused by T1 and T2 changes, rather than variations in metabolite concentrations given the short repetition time and long echo time values used, our findings point to thalamic dysfunction, corroborating prior evidence.

Prevalence of metabolic syndrome and its associated factors in renal transplant recipients

INTRODUCTION: The population of patients undergoing renal transplantation is considered at highrisk for developing obesity and changes in lipid and glucose metabolism, due to the use of immunosuppressive drugs and increased food freedom in the post-transplant period. OBJECTIVE: This study was designed to assess the prevalence of metabolic syndrome in renal transplant recipients and to identify factors associated with its occurrence. METHODS: A cross-sectional study was performed in renal transplant patients, with more than six months of follow-up. The metabolic syndrome was diagnosed according to the criteria of the National Cholesterol Education Program Adult Treatment Panel III. RESULTS: Among the 87 pa- tients enrolled, 39 (44.8%) presented the phenotype of metabolic syndrome. The mean age of the patients was 43.5 ± 12.1 years-old, with a predominance of male (69.0%) and white (66.7%). The mean and median times of post transplant follow-up were 64.2 ± 49.4 and 56 months, respectively. All the 12 patients who developed post-transplant diabetes mellitus also met the criteria for metabolic syndrome, which compromised the inclusion of this variable in the logistic regression. In the univariate analysis, patients with metabolic syndrome had higher mean age (p = 0.008), higher median blood level of cyclosporine (p = 0.021), higher prevalence of history of coronary disease (p = 0.023), and they were more frequent users of beta (p = 0.011) and calcium- channel blockers (p = 0.039). In the multivariate analysis, age (HR = 1.06; 95% CI=1.01-1.11, p=0.006) and use of beta-blockers (HR = 4.02; 95% CI = 1.41 - 11.4, p = 0.009) were asso- ciated with increased risk of metabolic syndrome. CONCLUSION: Metabolic syndrome was highly prevalent in the population of renal trans- plant recipients studied, and it was associated with older age, use of beta-blockers, and post-transplant diabetes mellitus.