Abstract

The objectives of these experiments were as follows: (1) to determine the association between circulating concentrations of pregnancy-associated glycoproteins (PAG) and late embryonic mortality (EM) in lactating dairy cattle following fixed-time artificial insemination (TAI) on d 0 or timed embryo transfer (TET) on d 7, (2) to identify a circulating concentration of PAG on d 31 below which late EM would be likely to occur, and (3) to identify when during gestation (d 31–59) late EM is occurring. Cows were diagnosed pregnant on d 31 of gestation based on presence of a fetal heartbeat and reconfirmed to be pregnant on d 59 of gestation. Late EM occurred when a cow had a viable embryo on d 31 of gestation but not on d 59 following TAI or TET. Only pregnant cows on d 31 were included in the analysis (TAI-maintained, n=413; TAI-EM, n=77; TET-maintained, n=238; TET-EM, n=47). Cows that were pregnant at d 31 of gestation and maintained the pregnancy until d 59 had significantly higher circulating concentrations of PAG at d 31 of gestation compared with cows that experienced late EM between d 31 and 59 of gestation in both TAI and TET. To conduct a more stringent test of the effectiveness of a single circulating PAG concentration (d 31) to predict EM, a receiver-operating characteristic curve was generated to identify a PAG concentration on d 31 that would predict EM with ≥95% accuracy in cows that received TAI or TET. Based on positive and negative predicative value analysis, a circulating concentration of PAG below 1.4ng/mL (TAI; minimal detectable level 0.28ng/mL) and 1.85ng/mL (TET) was 95% accurate in predicting EM (between d 31 and 59) at d 31 of gestation, respectively. Following TET, embryonic loss was tracked by Doppler ultrasound, progesterone, and PAG from d 24 to 59 of gestation, with more than 50% of the loss occurring between d 31 and 38 of gestation. In summary, circulating concentrations of PAG on d 31 of gestation may provide a good marker for predicting EM between d 31 and 59 of gestation, and the data suggest that this model could help predict which cows will undergo late EM.

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