Effects of a 1-h cycling trial on post-exercise plasma metabolomics after 2-week supplementation with dairy products-based, high-flavonoid exercise recovery drink: a randomised controlled crossover trial.

Content of image described in text. Fruit-derived flavonoids may enhance exercise performance and/or improve recovery due to their antioxidant and anti-inflammatory activities. Evidence in humans suggests that supplementation with about 300 mg of flavonoids before exercise may affect exercise perfor...

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Detalles Bibliográficos
Publicado en:British Journal of Nutrition Vol. 136; no. 2; pp. 178 - 191
Autores principales: Kung, Stephanie, Kim, Youngwook, Bressel, Eadric, Lefevre, Michael, Ward, Robert E.
Formato: research tables/charts randomized controlled trial Journal Article
Publicado: Cambridge University Press 7/28/2026
Acceso en línea:Ver este registro en EBSCOhost
Descripción
Sumario:Content of image described in text. Fruit-derived flavonoids may enhance exercise performance and/or improve recovery due to their antioxidant and anti-inflammatory activities. Evidence in humans suggests that supplementation with about 300 mg of flavonoids before exercise may affect exercise performance and recovery. The aim of this study was to evaluate the plasma metabolomic response to a 1-h cycling trial after twelve participants had consumed either a high or low dairy milk-based flavonoid (490 or 5 mg) pre-workout beverage for 15 d. A randomised, double-blind, placebo-controlled design was used, and subjects completed a submaximal cycling trial (45 m 70 % VO2 max, 15 m time trial). Plasma was collected before and after the exercise trial and at 1-h and 4-h post-exercise. No statistically significant difference was observed (P = 0·051), but a small effect size (d = 0·16) suggests a marginal trend towards increased power output during cycling with the treatment. Plasma samples were extracted, derivatised and subjected to GC-MS-based metabolomics analysis. Sixty-two metabolites were measured, of which forty-two were identified, and twenty are unknowns. A two-way repeated ANOVA with log-transformed and auto-scaled values indicated that 56 of the 62 features were significantly different with respect to time, but no significant treatment effects or treatment-by-time interactions were observed. Using the Euclidean distance measure and Ward clustering algorithm, a heatmap was generated that divided the metabolite response into eight groups and sixteen subgroups. Metabolites (carbohydrates, lipids and amino acids) changed to varying degrees in response to exercise, suggesting that multiple fuel substrate pathways were activated throughout exercise and recovery.