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Human skeletal muscle nitrate store: influence of dietary nitrate supplementation and exercise.

Lee J WylieJi Won ParkAnni VanhataloStefan KadachMatthew I BlackZdravko StoyanovAlan N SchechterAndrew M JonesBarbora Piknova
Published in: The Journal of physiology (2019)
Rodent skeletal muscle contains a large store of nitrate that can be augmented by the consumption of dietary nitrate. This muscle nitrate reservoir has been found to be an important source of nitrite and nitric oxide (NO) via its reduction by tissue xanthine oxidoreductase. To explore if this pathway is also active in human skeletal muscle during exercise, and if it is sensitive to local nitrate availability, we assessed exercise-induced changes in muscle nitrate and nitrite concentrations in young healthy humans, under baseline conditions and following dietary nitrate consumption. We found that baseline nitrate and nitrite concentrations were far higher in muscle than in plasma (∼4-fold and ∼29-fold, respectively), and that the consumption of a single bolus of dietary nitrate (12.8 mmol) significantly elevated nitrate concentration in both plasma (∼19-fold) and muscle (∼5-fold). Consistent with these observations, and with previous suggestions of active muscle nitrate transport, we present western blot data to show significant expression of the active nitrate/nitrite transporter sialin in human skeletal muscle. Furthermore, we report an exercise-induced reduction in human muscle nitrate concentration (by ∼39%), but only in the presence of an increased muscle nitrate store. Our results indicate that human skeletal muscle nitrate stores are sensitive to dietary nitrate intake and may contribute to NO generation during exercise. Together, these findings suggest that skeletal muscle plays an important role in the transport, storage and metabolism of nitrate in humans.
Keyphrases
  • nitric oxide
  • skeletal muscle
  • drinking water
  • nitric oxide synthase
  • insulin resistance
  • endothelial cells
  • hydrogen peroxide
  • physical activity
  • type diabetes
  • machine learning
  • pluripotent stem cells
  • body composition