METHODS: The aqueous ethanolic leaf extracts of C. caudatus were characterized by NMR and LC-MS/MS. The total phenolic content and α-glucosidase inhibitory activity were evaluated by the Folin-Ciocalteu method and α-glucosidase inhibitory assay, respectively. The statistical significance of the results was evaluated using one-way ANOVA with Duncan's post hoc test, and correlation among the different activities was performed by Pearson's correlation test. NMR spectroscopy along with multivariate data analysis was used to identify the metabolites correlated with total phenolic content and α-glucosidase inhibitory activity of the C. caudatus leaf extracts.
RESULTS: It was found that the α-glucosidase inhibitory activity and total phenolic content of the optimized ethanol:water (80:20) leaf extract of the plant increased significantly as the plant matured, reaching a maximum at the 10th week. The IC50 value for α-glucosidase inhibitory activity (39.18 μg mL- 1) at the 10th week showed greater potency than the positive standard, quercetin (110.50 μg mL- 1). Through an 1H NMR-based metabolomics approach, the 10-week-old samples were shown to be correlated with a high total phenolic content and α-glucosidase inhibitory activity. From the partial least squares biplot, rutin and flavonoid glycosides, consisting of quercetin 3-O-arabinofuranoside, quercetin 3-O-rhamnoside, quercetin 3-O-glucoside, and quercetin 3-O-xyloside, were identified as the major bioactive metabolites. The metabolites were identified by NMR spectroscopy (J-resolve, HSQC and HMBC experiments) and further supported by dereplication via LC-MS/MS.
CONCLUSION: For high phytomedicinal quality, the 10th week is recommended as the best time to harvest C. caudatus leaves with respect to its glucose lowering potential.
METHODS: The mid-stream urine was collected from 96 patients diagnosed with dengue fever at Penang General Hospital (PGH) and 50 healthy volunteers. Urine samples were analyzed with proton nuclear magnetic resonance (1H NMR) spectroscopy, followed by chemometric multivariate analysis. NMR signals highlighted in the orthogonal partial least square-discriminant analysis (OPLS-DA) S-plots were selected and identified using Human Metabolome Database (HMDB) and Chenomx Profiler. A highly predictive model was constructed from urine profile of dengue infected patients versus healthy individuals with the total R2Y (cum) value 0.935, and the total Q2Y (cum) value 0.832.
RESULTS: Data showed that dengue infection is related to amino acid metabolism, tricarboxylic acid intermediates cycle and β-oxidation of fatty acids. Distinct variations in certain metabolites were recorded in infected patients including amino acids, various organic acids, betaine, valerylglycine, myo-inositol and glycine.
CONCLUSION: Metabolomics approach provides essential insight into host metabolic disturbances following dengue infection.