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  • Title: Studying the effects of natural extracts with metabolomics: A longitudinal study on the supplementation of healthy rats with Polygonum cuspidatum Sieb. et Zucc.
    Author: Peron G, Uddin J, Stocchero M, Mammi S, Schievano E, Dall'Acqua S.
    Journal: J Pharm Biomed Anal; 2017 Jun 05; 140():62-70. PubMed ID: 28340474.
    Abstract:
    BACKGROUND: A longitudinal study was performed to evaluate the effects of Polygonum cuspidatum extract (standardized at 20% resveratrol) supplementation on healthy rats. The effects were explored by monitoring urinary metabolome changes using UPLC-HRMS and 1H NMR-based approaches. The aim of the study was to explore the effects of P. cuspidatum supplementation on a healthy animal model using metabolomics, in order to determine possible modes of action and obtain information on bioactivity. METHODS: Healthy Sprague-Dawley rats were orally supplemented with 100mg/kg of dried P. cuspidatum extract for 49days and 24-h urinary outputs were collected. Samples were analysed by untargeted UPLC-HRMS and 1H NMR approaches and the obtained data sets were modelled by an adaptation of post-transformation of PLS2 to longitudinal studies. Putative markers were discovered by a stability selection procedure and specific oxidative stress markers were monitored by a targeted HPLC-MS/MS analysis to assess the in vivo antioxidant activity of P. cuspidatum extract. RESULTS: UPLC-HRMS and 1H NMR platforms showed two different but complementary patterns of metabolites describing the changes ascribable to P. cuspidatum supplementation and using both approaches, a comprehensive resveratrol metabolism and urinary excretion could be observed. Markers of P. cuspidatum supplementation effects identified by UPLC-HRMS were mainly related to its antioxidant activity and to a possible "adaptogenic" activity. Urinary changes observed by 1H NMR were mainly related to energy metabolism. UPLC-HRMS and 1H NMR metabolomics approaches allowed the effects of a prolonged supplementation with P. cuspidatum on healthy rats to be observed. The statistical models built from both data sets showed metabolic changes in urines related to rat aging.
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