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Arginine for gestating sows and foetal development: A systematic review.

J Y P PalenciaM A G LemesC A P GarbossaM L T AbreuL J PereiraMarcio Gilberto Zangeronimo
Published in: Journal of animal physiology and animal nutrition (2017)
The use of functional amino acids during pregnancy has been linked to improved reproduction in mammals. In this context, arginine is a precursor in the synthesis of numerous molecules, such as nitric oxide and polyamines, which play an important role during reproduction. However, contradictory studies are found in the literature, particularly regarding the amount of supplementation and the period of pregnancy in which it is used. The objective of this study was to evaluate the effects of dietary arginine supplementation for pregnant sows on foetal development via a systematic review. The search for papers was performed during the month of December 2015, in the databases ISI Web of Science, Science Direct, Scopus, and SciELO. From a total of 5675 returned studies, only 13 papers were selected after applying selection criteria. Most (47%) of the studies that evaluated the effects of dietary arginine supplementation on foetal development in pigs used 1% arginine. Supplementation was initiated in the first third of pregnancy in 47% of tests, including in both primiparous and multiparous sows. These studies showed positive results for embryo survival and foetal development, evidenced by the increase in placental weight and the number and weight of piglets born alive. Of all evaluated studies, 53% showed benefits on foetal development. It is concluded that supplementing dietary arginine in gestating sows can benefit embryo survival and foetal development. However, to establish a supplementation plan with this amino acid, aspects related to the period of pregnancy, supplementation levels, and source of arginine must be well defined.
Keyphrases
  • nitric oxide
  • amino acid
  • gestational age
  • case control
  • body mass index
  • pregnancy outcomes
  • physical activity
  • pregnant women
  • machine learning
  • body weight