ANTI-DIABETIC AND ANTI-OXIDANT EVALUATION OF ANTHOCLEISTA DJALONENSIS A. CHEV AND ANTHOCLEISTA VOGELII PLANCH

Diabetes a major degenerative disease of global concern accounts for about 3.2 million deaths annually. Alpha-amylase inhibitors from plants are effective in managing postprandial hyperglycaemia which is significant in Type 2 diabetes. Search for natural anti-oxidants has increased recently because free radicals production has been linked to a number of diseases including diabetes. Anthocleista djalonensis and Anthocleista vogelii are used traditionally in Nigeria and parts of Africa to treat diabetes. This study was aimed at evaluating the α-amylase inhibition, anti-oxidant and anti-diabetic effects of extracts and compounds of both plants to verify their traditional use. The leaves, stem bark and roots of both plants were collected along Ijebu-Ode – Benin road and authenticated at the Herbarium of the Forestry Research Institute of Nigeria, Ibadan. The plant samples were macerated in 80% aqueous methanol for 72 h. Each crude extract, suspended in water: methanol (4:1) was partitioned into ethyl acetate. The crude extracts and ethyl acetate fractions of the leaves and stem bark of both plants were subjected to in vitro α-amylase inhibition assay with acarbose as positive control. The anti-oxidant activity was evaluated using 2, 2‟-diphenyl-1-picrylhydrazyl with α- tocopherol as control, while anti-diabetic properties of the crude extracts were studied in vivo using 45 albino wistar rats (150-200 g) of both sexes. The rats were made diabetic with 80 mg/kg of alloxan and treated with the extracts (1 g/kg) for seven days; glibenclamide 2.5 mg/kg was used as reference. Blood glucose levels (BGL) were monitored daily. Bioassay-guided fractionation and chromatographic methods were used to isolate active compounds from the ethyl acetate fractions of both plants. Structures of the isolated compounds were elucidated using spectroscopic techniques: infra-red, mass spectrometry, nuclear magnetic resonance (one-dimensional and twodimensional). Data were analysed statistically using ANOVA at p