Associations of inflammatory and reproductive tract disorders postpartum with pregnancy and early pregnancy loss in dairy cows

Metritis and the uterine disease microbiome are associated with long-term changes in the endometrium of dairy cows†

Cows with metritis (uterine disease) during the first 1 to 2 weeks postpartum have lower pregnancy rates when inseminated later postpartum (typically >10 weeks). We hypothesized that metritis and the disease-associated uterine microbiome have a long-term effect on endometrial gene expression. Changes in gene expression may inform a mechanism through which disease lowers pregnancy rates. A total of 20 cows were enrolled at 1 to 2 weeks postpartum to either metritis (clinical disease; n = 10) or healthy (control; n = 10) groups and randomly assigned to be slaughtered at approximately 80 and 165 dpp (mid-lactation). The microbiome of the reproductive tract was sampled to confirm the presence of pathogens that are typical of metritis. In addition to the original clinical diagnosis, study cows were retrospectively assigned to uterine-disease and control groups based on the composition of their microbiome. There was no effect of early postpartum uterine disease on the uterine microbiome at mid-lactation (time of slaughter). Nonetheless, early postpartum metritis and the disease microbiome were associated with a large number of differentially-expressed genes at mid-lactation primarily in the caruncular compared with the inter-caruncular endometrium. Gene enrichment analysis identified oxidative phosphorylation as the primary pathway increased in caruncular endometrium of diseased cows whereas growth factor signaling pathways were reduced. The current study demonstrated that metritis and a uterine disease microbiome leave a sustained imprint on gene expression in the caruncular endometrium that may explain lower fertility in cows with postpartum uterine disease.

Accuracy of early pregnancy diagnosis and determining pregnancy loss using different biomarkers and machine learning applications in dairy cattle

This study aimed to compare the accuracy of IFN-τ stimulated gene abundance (ISGs) in peripheral blood mononuclear cells (PBMCs), CL blood perfusion by Doppler ultrasound (Doppler-US), plasma concentration of P4 on Day 21 and pregnancy-associated glycoproteins (PAGs) test on Day 25 after timed-artificial insemination (TAI) for early pregnancy diagnosis in dairy cows and heifers. Holstein cows (n = 140) and heifers (n = 32) were subjected to a hormonal synchronization protocol and TAI on Day 0. On Day 21 post-TAI, blood samples were collected for PBMC isolation and plasma concentration of P4. The CL blood perfusion was evaluated by Doppler-US. Plasma samples collected on Day 25 were assayed for PAGs. The abundance of ISGs (ISG15 and RSAD2) in PBMCs was determined by RT-qPCR. Pregnancy was confirmed on Days 32 and 60 post-TAI by B-mode ultrasonography. Statistical analyses were performed by ANOVA using the MIXED procedure and GLIMMIX in SAS software. The pregnancy biomarkers were used to categorize the females as having undergone late luteolysis (LL); early embryonic mortality (EEM); late embryonic mortality (LEM); or late pregnancy loss (LPL). The abundance of ISGs, CL blood perfusion by Doppler-US, and concentrations of P4 on Day 21, and PAGs test on Day 25 were significant (P < 0.05) predictors of early pregnancy in dairy cows and heifers. Dairy cows had a greater (P = 0.01) occurrence of LL than heifers, but there was no difference (P > 0.1) for EEM, LEM, and LPL in heifers compared to cows. Cows with postpartum reproductive issues had a greater (P = 0.008) rate of LEM and a lesser (P = 0.01) rate of LPL compared to cows without reproductive issues. In summary, the CL blood perfusion by Doppler-US had the highest accuracy and the least number of false negatives, suggesting it is the best predictor of pregnancy on Day 21 post-TAI. The PAGs test was the most reliable indicator of pregnancy status on Day 25 post-TAI in dairy heifers and cows. The application of machine learning, specifically the MARS algorithm, shows promise in enhancing the accuracy of predicting early pregnancies in cows.

Application of behavior data to predictive exploratory models of metritis self-cure and treatment failure in dairy cows

The objective was to evaluate the performance of exploratory models containing routinely available on-farm data, behavior data, and the combination of both to predict metritis self-cure (SC) and treatment failure (TF). Holstein cows (n = 1,061) were fitted with a collar-mounted automated-health monitoring device (AHMD) from -21 ± 3 to 60 ± 3 d relative to calving to monitor rumination time and activity. Cows were examined for diagnosis of metritis at 4 ± 1, 7 ± 1, and 9 ± 1 d in milk (DIM). Cows diagnosed with metritis (n = 132), characterized by watery, fetid, reddish/brownish vaginal discharge (VD), were randomly allocated to 1 of 2 treatments: control (CON; n = 62), no treatment at the time of metritis diagnosis (d 0); or ceftiofur (CEF; n = 70), subcutaneous injection of 6.6 mg/kg of ceftiofur crystalline-free acid on d 0 and 3 relative to diagnosis. Cure was determined 12 d after diagnosis and was considered when VD became mucoid and not fetid. Cows in CON were used to determine SC, and cows in CEF were used to determine TF. Univariable analyses were performed using farm-collected data (parity, calving season, calving-related disorders, body condition score, rectal temperature, and DIM at metritis diagnosis) and behavior data (i.e., daily averages of rumination time, activity generated by AHMD, and derived variables) to assess their association with metritis SC or TF. Variables with P-values ≤0.20 were included in the multivariable logistic regression exploratory models. To predict SC, the area under the curve (AUC) for the exploratory model containing only data routinely available on-farm was 0.75. The final exploratory model to predict SC combining routinely available on-farm data and behavior data increased the AUC to 0.87, with sensitivity (Se) of 89% and specificity (Sp) of 77%. To predict TF, the AUC for the exploratory model containing only data routinely available on-farm was 0.90. The final exploratory model combining routinely available on-farm data and behavior data increased the AUC to 0.93, with Se of 93% and Sp of 87%. Cross-validation analysis revealed that generalizability of the exploratory models was poor, which indicates that the findings are applicable to the conditions of the present exploratory study. In summary, the addition of behavior data contributed to increasing the prediction of SC and TF. Developing and validating accurate prediction models for SC could lead to a reduction in antimicrobial use, whereas accurate prediction of cows that would have TF may allow for better management decisions.

Association of uterine health in the first lactation with transition cow health and reproductive performance in the second lactation of Holstein dairy cows

The objective of this study was to evaluate the effects of puerperal metritis (PM) diagnosed and treated during the early postpartum period of the first lactation on transition cow health, milk production, reproduction, and culling of dairy cows in their second lactation. Diagnosis of PM was based on fetid watery red-brown uterine discharge and rectal temperature above 39.5°C. Two farms were enrolled in this retrospective observational cohort study (Farm A and B). In both farms, the following diseases were recorded during the first 30 DIM in lactation 1 and 2: clinical hypocalcemia (CH), retained fetal membrane (RFM), PM, hyperketonemia (KET), left displaced abomasum (LDA) and clinical mastitis (MAST). Statistical analyses were performed using SPSS for Windows separately for each farm. Linear and logistic regression models were used for continuous (e.g., milk yield) and binary (e.g., disease, pregnancy per AI, pregnancy loss) outcomes, respectively. Cox proportional hazard regression models were calculated to model the time to event outcomes for culling or death during the first 60 DIM and for pregnancy within 250 d of the second lactation. The initial models contained the following variables: year of calving, month of calving, calving ease, stillbirth, twins, days open in lactation 1, 305 d milk yield in lactation 1, PM in lactation 1, and PM in lactation 2 as explanatory variables. A total of 4,834 cows (Farm A) and 4,238 cows (Farm B) in the second lactation were considered for statistical analyses. On farm A, the incidence of PM in lactation 1 and 2 was 20.1% and 11.2%, respectively. On farm B, the incidence of PM in lactation 1 and 2 was 14.4% and 8.5%, respectively. On both farms, cows with PM in their first lactation had greater odds for RFM and PM in their second lactation, while there was no association of PM in the first lactation with any other non-uterine diseases (i.e., CH, KET, LDA, and MAST) in the second lactation. Cows with PM in lactation 2 had reduced milk yield. The reduction in milk yield in second lactation was greater for cows that already experienced PM in lactation 1. On Farm A, cows with PM in their first lactation had a greater hazard for culling within 60 DIM of the second lactation; however, the same association was not present on Farm B. Cows with PM in lactation 1 had reduced pregnancy per AI at first service in the second lactation only on farm B. Cows with PM in lactation 2 had reduced pregnancy per AI at first service in the second lactation on both farms. Pregnancy loss in lactation 2 was only associated with PM in lactation 2 but not with PM in lactation 1. On both farms, cows had a reduced hazard for pregnancy in their second lactation within 250 DIM when they experienced PM in either lactation. In conclusion, PM in the first lactation had long-lasting negative consequences (i.e., risk of uterine disease and lower reproductive performance) for cows in their next lactation.

Graduate Student Literature Review: Implications of transition cow health for reproductive function and targeted reproductive management

Negative associations of health disorders with reproductive performance, often measured with pregnancy risk per artificial insemination (AI) or the risk of pregnancy loss, have been demonstrated extensively. Most studies investigated common clinical diseases but did not include subclinical disorders comprehensively. They often evaluated cows subjected to hormonal synchronization protocols for timed AI, limiting the ability to understand how disease may affect spontaneous reproductive function, which is essential for targeted management programs with selective hormonal intervention. It is plausible that metabolic and inflammatory disorders have short- and long-term detrimental effects on different features of reproductive function that result in or contribute to reduced fertility. These may include: 1) reestablishment of endocrine function to promote follicular growth and first ovulation postpartum, 2) corpus luteum (CL) function, 3) estrus expression, and 4) uterine environment, fertilization, and embryonic development. In this narrative literature review, we discuss insights and knowledge gaps linking health disorders with these processes of reproductive function. A growing set of observational studies with adequate internal validity suggest that these outcomes may be affected by metabolic and inflammatory disorders that are common in the early postpartum period. A better characterization of these risk factors in multi-site studies with greater external validity is warranted to develop decision-support tools to identify subgroups of cows that are more or less likely to be successful in targeted reproductive management programs.