Using Pregnancy-Associated Glycoproteins (PAGs) to Improve Reproductive Management: From Dairy Cows to Other Dairy Livestock

Simple Summary Pregnancy loss is a major cause of infertility in dairy animals, particularly in cattle, which affects the productivity and profitability of farms. Detecting these unsuccessful pregnancies could offer farmers the opportunity to reduce the economic damage caused by pregnancy loss. The determination of proteins secreted by the placenta and related to the presence of a viable conceptus called pregnancy-associated glycoproteins (PAGs) represents a diagnostic tool to identify pregnant or non-pregnant animals and to predict early pregnancy failures. This review describes the state of the art related to PAGs' function, pregnancy profile, and use in reproductive management in bovine and other dairy livestock. Pregnancy success represents a major issue for the economic income of cattle breeders. Early detection of pregnant and non-pregnant animals, as well as the prediction of early pregnancy failure, can influence farm management decisions. Several diagnostic tools for pregnancy are currently available. Among these, pregnancy-associated glycoproteins (PAGs) have been shown to be useful for identifying the presence of vital embryos and for pregnancy follow-up monitoring. This review presents an overview of the PAGs' functions, their pregnancy trends, and their use as a tool to improve reproductive management in bovine and other dairy livestock, such as small ruminants and buffalos.

Automated summary

Pregnancy loss is a significant issue in dairy animals, particularly cattle, impacting farm productivity and profitability. The use of pregnancy-associated glycoproteins (PAGs) as a diagnostic tool to detect successful and unsuccessful pregnancies has the potential to reduce economic damage caused by pregnancy loss. This review provides an overview of PAGs' function, pregnancy profile, and their use in reproductive management in bovine and other dairy livestock. Early detection of pregnancy status and prediction of pregnancy failure can influence farm management decisions.