PURPOSE: This study provides new insights on the changes of endogenous metabolites caused by I. aquatica ethanolic extract and improves the understanding on the therapeutic efficacy and mechanism of I. aquatica ethanolic extract.
METHODS: By using a combination of 1H nuclear magnetic resonance (NMR) with multivariate analysis (MVDA), the changes of metabolites due to I. aquatica ethanolic extract administration in obese diabetic-induced Sprague Dawley rats (OB+STZ+IA) were identified.
RESULTS: The results suggested 19 potential biomarkers with variable importance projections (VIP) above 0.5, which include creatine/creatinine, glucose, creatinine, citrate, carnitine, 2-oxoglutarate, succinate, hippurate, leucine, 1-methylnicotinamice (MNA), taurine, 3-hydroxybutyrate (3-HB), tryptophan, lysine, trigonelline, allantoin, formiate, acetoacetate (AcAc) and dimethylamine. From the changes in the metabolites, the affected pathways and aspects of metabolism were identified.
CONCLUSION: I. aquatica ethanolic extract increases metabolite levels such as creatinine/creatine, carnitine, MNA, trigonelline, leucine, lysine, 3-HB and decreases metabolite levels, including glucose and tricarboxylic acid (TCA) intermediates. This implies capabilities of I. aquatica ethanolic extract promoting glycolysis, gut microbiota and nicotinate/nicotinamide metabolism, improving the glomerular filtration rate (GFR) and reducing the β-oxidation rate. However, the administration of I. aquatica ethanolic extract has several drawbacks, such as unimproved changes in amino acid metabolism, especially in reducing branched chain amino acid (BCAA) synthesis pathways and lipid metabolism.
RESULTS: Comparison of the PLS and RF showed that RF exhibited poorer generalization and hence poorer predictive performance. Both the regression coefficient of PLS and the variable importance of RF revealed that quercetin and kaempferol derivatives, caffeic acid and vitexin-2-O-rhamnoside were significant towards the tested bioactivities. Furthermore, principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA) results showed that sonication and absolute ethanol are the preferable extraction method and ethanol ratio, respectively, to produce N. oleracea extracts with high phenolic levels and therefore high DPPH scavenging and α-glucosidase inhibitory activities.
CONCLUSION: Both PLS and RF are useful regression models in metabolomics studies. This work provides insight into the performance of different multivariate data analysis tools and the effects of different extraction conditions on the extraction of desired phenolics from plants. © 2017 Society of Chemical Industry.