METHODS: 1H-MRS utilising the Single-Voxel Spectroscopy (SVS) technique was performed using a 3.0Tesla MRI on 45 optic radiations (15 from healthy subjects, 15 from mild glaucoma patients, and 15 from severe glaucoma patients). A standardised Volume of Interest (VOI) of 20 × 20 × 20 mm was placed in the region of optic radiation. Mild and severe glaucoma patients were categorised based on the Hodapp-Parrish-Anderson (HPA) classification. Mean and multiple group comparisons for metabolite concentration and metabolite concentration ratio between glaucoma grades and healthy subjects were obtained using one-way ANOVA.
RESULTS: The metabolite concentration and metabolite concentration ratio between the optic radiations of glaucoma patients and healthy subjects did not demonstrate any significant difference (p > 0.05).
CONCLUSION: Our findings show no significant alteration of metabolite concentration associated with neurodegeneration that could be measured by single-voxel 1H-MRS in optic radiation among glaucoma patients.
KEY POINTS: • Glaucoma disease has a neurodegenerative component. • Metabolite changes have been observed in the neurodegenerative process in the brain. • Using SVS, no metabolite changes in optic radiation were attributed to glaucoma.
OBJECTIVES: To identify novel biomarkers able to discriminate between alcohol-dependent, non-AD alcohol drinkers and controls using metabolomics.
METHOD: Urine samples were collected from 30 alcohol-dependent persons who did not yet start AD treatment, 54 social drinkers and 60 controls, who were then analysed using NMR. Data analysis was done using multivariate analysis including principal component analysis (PCA) and orthogonal partial least square-discriminate analysis (OPLS-DA), followed by univariate and multivariate logistic regression to develop the discriminatory model. The reproducibility was done using intraclass correlation coefficient (ICC).
RESULTS: The OPLS-DA revealed significant discrimination between AD and other groups with sensitivity 86.21%, specificity 97.25% and accuracy 94.93%. Six biomarkers were significantly associated with AD in the multivariate logistic regression model. These biomarkers were cis-aconitic acid, citric acid, alanine, lactic acid, 1,2-propanediol and 2-hydroxyisovaleric acid. The reproducibility of all biomarkers was excellent (0.81-1.0).
CONCLUSION: This study revealed that metabolomics analysis of urine using NMR identified AD novel biomarkers which can discriminate AD from social drinkers and controls with high accuracy.
METHODS: A cross sectional study was conducted on three groups: individuals with alcohol use disorders (n=30), social drinkers (n=54) and alcohol-naive controls (n=60). 1H NMR-based metabolomics was used to obtain the metabolic profiles of plasma samples. Data were processed by multivariate principal component analysis (PCA) and orthogonal partial least squares-discriminant analysis (OPLS-DA) followed by univariate and multivariate logistic regressions to produce the best fit-model for discrimination between groups.
RESULTS: The OPLS-DA model was able to distinguish between the AUD group and the other groups with high sensitivity, specificity and accuracy of 64.29%, 98.17% and 91.24% respectively. The logistic regression model identified two biomarkers in plasma (propionic acid and acetic acid) as being significantly associated with alcohol use disorders. The reproducibility of all biomarkers was excellent (0.81-1.0).
CONCLUSIONS: The applied plasma metabolomics technique was able to differentiate the metabolites between AUD and the other groups. These metabolites are potential novel biomarkers for diagnosis of alcohol use disorders.
RESULTS: A total of 31 constituents comprising primary and secondary metabolites belonging to the chemical classes of fatty acids, amino acids, sugars, terpenoids and phenolic compounds were identified. Shade-dried leaves were identified to possess the highest concentrations of bioactive secondary metabolites such as chlorogenic acid, caffeic acid, luteolin, orthosiphol and apigenin, followed by microwave-dried samples. Freeze-dried leaves had higher concentrations of choline, amino acids leucine, alanine and glutamine and sugars such as fructose and α-glucose, but contained the lowest levels of secondary metabolites.
CONCLUSION: Metabolite profiling coupled with multivariate analysis identified shade drying as the best method to prepare OS leaves as Java tea or to include in traditional medicine preparation. © 2017 Society of Chemical Industry.
OBJECTIVE: Evaluate the relationship between the chemical composition of C. nutans and its anti-inflammatory properties using nuclear magnetic resonance (NMR) metabolomics approach.
METHODOLOGY: The anti-inflammatory effect of C. nutans air-dried leaves extracted using five different binary extraction solvent ratio and two extraction methods was determined based on their nitric oxide (NO) inhibition effect in lipopolysaccharide-interferon-gamma (LPS-IFN-γ) activated RAW 264.7 macrophages. The relationship between extract bioactivity and metabolite profiles and quantifications were established using 1 H-NMR metabolomics and liquid chromatography-tandem mass spectrometry (LC-MS/MS). The possible metabolite biosynthesis pathway was constructed to further strengthen the findings.
RESULTS: Water and sonication prepared air-dried leaves possessed the highest NO inhibition activity (IC50 = 190.43 ± 12.26 μg/mL, P