Metabolic analysis using HR-MAS in prostate tissue for prostate cancer diagnosis.

INTRODUCTION: In this study we used nuclear magnetic resonance spectroscopy in prostate tissue to provide new data on potential biomarkers of prostate cancer in patients eligible for prostate biopsy. MATERIAL AND METHODS: Core needle prostate tissue samples were obtained. After acquiring all the spectra using a Bruker Avance III DRX 600 spectrometer, tissue samples were subjected to routine histology to confirm presence or absence of prostate cancer. Univariate and multivariate analyses with metabolic and clinical variables were performed to predict the occurrence of prostate cancer. RESULTS: A total of 201 patients, were included in the study. Of all cores subjected to high-resolution magic angle spinning (HR-MAS) followed by standard histological study, 56 (27.8%) tested positive for carcinoma. According to HR-MAS probe analysis, metabolic pathways such as glycolysis, the Krebs cycle, and the metabolism of different amino acids were associated with presence of prostate cancer. Metabolites detected in tissue such as citrate or glycerol-3-phosphocholine, together with prostate volume and suspicious rectal examination, formed a predictive model for prostate cancer in tissue with an area under the curve of 0.87, a specificity of 94%, a positive predictive value of 80% and a negative predictive value of 84%. CONCLUSIONS: Metabolomics using HR-MAS analysis can uncover a specific metabolic fingerprint of prostate cancer in prostate tissue, using a tissue core obtained by transrectal biopsy. This specific fingerprint is based on levels of citrate, glycerol-3-phosphocholine, glycine, carnitine, and 0-phosphocholine. Several clinical variables, such as suspicious digital rectal examination and prostate volume, combined with these metabolites, form a predictive model to diagnose prostate cancer that has shown encouraging results.

Glutathione and a Pool of Metabolites Partly Related to Oxidative Stress Are Associated with Low and High Myopia in an Altered Bioenergetic Environment.

Oxidative stress forms part of the molecular basis contributing to the development and manifestation of myopia, a refractive error with associated pathology that is increasingly prevalent worldwide and that subsequently leads to an upsurge in degenerative visual impairment due to conditions that are especially associated with high myopia. The purpose of our study was to examine the interrelation of potential oxidative-stress-related metabolites found in the aqueous humor of high-myopic, low-myopic, and non-myopic patients within a clinical study. We conducted a cross-sectional study, selecting two sets of patients undergoing cataract surgery. The first set, which was used to analyze metabolites through an NMR assay, comprised 116 patients. A total of 59 metabolites were assigned and quantified. The PLS-DA score plot clearly showed a separation with minimal overlap between the HM and control samples. The PLS-DA model allowed us to determine 31 major metabolite differences in the aqueous humor of the study groups. Complementary statistical analysis of the data allowed us to determine six metabolites that presented significant differences among the experimental groups (p < 005). A significant number of these metabolites were discovered to have a direct or indirect connection to oxidative stress linked with conditions of myopic eyes. Notably, we identified metabolites associated with bioenergetic pathways and metabolites that have undergone methylation, along with choline and its derivatives. The second set consisted of 73 patients who underwent a glutathione assay. Here, we showed significant variations in both reduced and oxidized glutathione in aqueous humor among all patient groups (p < 0.01) for the first time. Axial length, refractive status, and complete ophthalmologic examination were also recorded, and interrelations among metabolic and clinical parameters were evaluated.

Salivary metabolomic profile associated with cariogenic risk in children.

OBJECTIVES: To identify the metabolomic differences in the saliva of healthy children versus children with active carious lesions and to estimate the predictive capacity of a model based on the salivary metabolomic profile. METHODS: A study of cases (n = 31) and controls (n = 37) was designed for children aged between 6 and 12 (mean age of the cases: 8.9; controls: 8.7). The said children attended public health centers in Valencia, Spain. Intraoral examinations were performed by a single examiner using ICDAS II diagnostic criteria. Unstimulated total saliva samples were analyzed by nuclear magnetic resonance (NMR) spectroscopy. RESULTS: The dft index for cases was 2.84 while it was 0.19 for the control group, the DMFT index was 1.13 and 0.11, respectively. The predictive model generated by the multivariate PLS-DA analysis projects a separation between the cases and the controls on the score chart with a predictive capacity and generating an area under the curve of 0.71. The metabolites: 3-methyl-2-oxovalerate, 3-hydroxybutyrate, lactate, acetone, citrate, ornithine, ethanolamine, taurine, proline, glycine, mannose, glucose, 1-6-Anhydro-ß-d-glucose and citraconate, are those that show greater significance in the model. In the controls, glycine (Cohen's d = 0.430) and glucose (Cohen's d = 0.560) present higher means compared to the cases. On the contrary, taurine (Cohen's d= -0.474) and mannose (Cohen's d= -0.456) show higher means in cases compared to controls. CONCLUSIONS: Our findings show a difference in the salivary metabolomic profiles, specifically in the groups of saccharides and amino acids, suggesting an association of these with the level of caries risk. CLINICAL SIGNIFICANCE: The results reported in the present study reinforce the use of salivary metabolomics as a research method for the search for salivary biomarkers that allow the evaluation of caries risk in patients. Furthermore, it brings us closer to a personalized medicine that will help in dental caries prevention strategies.

Using NMR in saliva to identify possible biomarkers of glioblastoma and chronic periodontitis.

Nowadays there is increasing interest in identifying-and using-metabolites that can be employed as biomarkers for diagnosing, treating and monitoring diseases. Saliva and NMR have been widely used for this purpose as they are fast and inexpensive methods. This case-control study aimed to find biomarkers that could be related to glioblastoma (GBL) and periodontal disease (PD) and studied a possible association between GBL and periodontal status. The participants numbered 130, of whom 10 were diagnosed with GBL and were assigned to the cases group, while the remaining 120 did not present any pathology and were assigned to the control group. On one hand, significantly increased (p < 0.05) metabolites were found in GBL group: leucine, valine, isoleucine, propionate, alanine, acetate, ethanolamine and sucrose. Moreover, a good tendency to separation between the two groups was observed on the scatterplot of the NMR. On the other hand, the distribution of the groups attending to the periodontal status was very similar and we didn´t find any association between GBL and periodontal status (Chi-Square 0.1968, p = 0.91). Subsequently, the sample as a whole (130 individuals) was divided into three groups by periodontal status in order to identify biomarkers for PD. Group 1 was composed of periodontally healthy individuals, group 2 had gingivitis or early periodontitis and group 3 had moderate to advanced periodontitis. On comparing periodontal status, a significant increase (p < 0.05) in certain metabolites was observed. These findings along with previous reports suggest that these could be used as biomarkers of a PD: caproate, isocaproate+butyrate, isovalerate, isopropanol+methanol, 4 aminobutyrate, choline, sucrose, sucrose-glucose-lysine, lactate-proline, lactate and proline. The scatter plot showed a good tendency to wards separation between group 1 and 3.