Changes in bovine leukemia virus serological status and lymphocyte count between dry-off and early lactation in Michigan dairy cows.

This study addresses how the serological status of bovine leukemia virus (BLV) and lymphocyte count fluctuate from dry-off to early lactation in dairy cattle. Very few studies have investigated how BLV antibody status and lymphocyte count of cows changes longitudinally during the lactation cycle. Blood samples were collected from dairy cattle (n = 149) on 5 commercial dairy herds in Michigan at dry-off, close-up, and 7 to 10 d after calving. Plasma was analyzed for anti-BLV antibodies using a BLV-ELISA and whole blood was analyzed for lymphocyte counts. We found that BLV seroprevalence increased from dry-off (38.9%) to close-up (43.6%), then slightly decreased from close-up to 7 to 10 d after calving (43.0%). However, the change in seroprevalence was only significant from dry-off to close-up. Cows of third or higher parity were more likely to seroconvert than cows of lower parity and had the highest ELISA-negative prevalence of BLV. Lymphocyte counts were significantly higher in ELISA-positive animals, but only among second and third or greater parity animals. These results indicate that the use of lymphocyte counts as a disease severity monitoring tool for BLV should differ by parity group. Future studies should investigate if changes in seroprevalence are due to new infections or natural changes in antibody concentrations as the cow prepares for colostrum production. More accurate lymphocyte guidelines to be used for monitoring the progression of BLV should be created that consider parity and lactation stage.

Serum vitamin D concentrations at dry-off and close-up predict increased postpartum urine ketone concentrations in dairy cattle.

Vitamin D is commonly supplemented to dairy cows as vitamin D3 to support calcium homeostasis and in times of low sunlight exposure. Vitamin D has beneficial immunomodulatory and anti-inflammatory properties. Serum 25-hydroxyvitamin D [25(OH)D] concentrations fluctuated during lactation, with the lowest concentrations measured in healthy cows within 7 d of calving. However, it is unknown if serum 25(OH)D concentrations measured during the previous lactation are associated with transition diseases or health risk factors in dairy cattle. We collected serum samples from 279 dairy cattle from 5 commercial dairy herds in Michigan at dry-off, close-up, and 2-10 d in milk (DIM). Vitamin D concentrations were determined by measuring serum 25(OH)D by radioimmunoassay. Total serum calcium was measured by colorimetric methods. Body condition scores (BCS) were assigned at the time of blood collection. Clinical disease incidence was monitored until 30 d postparturition. Separate bivariable logistic regression analyses were used to determine if serum 25(OH)D at dry-off, close-up, and 2-10 DIM was associated with various clinical diseases including mastitis, lameness, and uterine disorders (classified as metritis, retained placenta, or both) and increased urine ketone concentrations at P < 0.05. Among all significant bivariable analyses, multivariable logistic regression analyses were built to adjust for potential confounding variables including parity, BCS, season, and calcium. Receiver operator characteristic (ROC) curve analyses were used to determine optimal concentrations of serum 25(OH)D. We found that higher serum 25(OH)D concentrations at dry-off and close-up predicted increased urine ketone concentrations in early lactation, even after adjustment for confounders. Alternatively, we found that lower serum 25(OH)D at 2-10 DIM was associated with uterine diseases. Optimal concentrations for serum 25(OH)D at dry-off and close-up for lower risk of increased urine ketone concentrations were below 103.4 and 91.1 ng/mL, respectively. The optimal concentration for serum 25(OH)D at 2-10 DIM for uterine diseases was above 71.4 ng/mL. These results indicate that serum 25(OH)D at dry-off and close-up may be a novel predictive biomarker for increased urine ketone concentrations during early lactation. Increased urine ketone concentrations are not necessarily harmful or diagnostic for ketosis but do indicate development of negative energy balance, metabolic stress, and increased risk of early lactation diseases. Predicting that dairy cattle are at increased risk of disease facilitates implementation of intervention strategies that may lower disease incidence. Future studies should confirm these findings and determine the utility of serum 25(OH)D concentrations as a predictive biomarker for clinical and subclinical ketosis.

Changes in biomarkers of nutrient metabolism, inflammation, and oxidative stress in dairy cows during the transition into the early dry period.

Metabolic stress occurs in dairy cows when physiologic homeostasis is disrupted as a consequence of aberrant nutrient metabolism, chronic inflammation, and oxidative stress. Early-lactation cows that suffer from metabolic stress are susceptible to health disorders that cause significant production losses. However, there is little information regarding the occurrence and effect of metabolic stress during involution. Therefore, the purpose of this study was to investigate well-known biomarkers associated with metabolic stress in early-lactation cows at various time points during the early dry period when dairy cows also are subjected to dramatic changes in physiologic homeostasis. Our group conducted a descriptive study by collecting serum and whole-blood samples from the coccygeal vein of 29 healthy dairy cows at a commercial dairy herd. Sampling points included d -6, 0, +1, +2, +6, and +12 relative to dry-off date. Samples were used to quantify biomarkers related to nutrient metabolism, oxidative stress, and inflammation that included calcium, nonesterified fatty acids, ß-hydroxybutyrate, albumin, haptoglobin, cortisol, reactive oxygen and nitrogen species, antioxidant potential, oxidant status index, and isoprostanes. Additionally, whole-blood leukocyte differentials for total leukocyte, neutrophils, lymphocytes, eosinophils, and monocytes were analyzed. Within altered nutrient metabolism biomarkers, calcium and nonesterified fatty acid concentrations changed most from d 0 to d +2 during the sampling period. Indicators of oxidant status, such as reactive oxygen and nitrogen species, antioxidant potential, and oxidant status index, generally increased throughout the sampling period except at d +2, suggesting altered redox status throughout early involution. In contrast, isoprostane concentrations fluctuated throughout the study, demonstrating that indicators of oxidative damage occurred more sporadically during the sampling period. Therefore, many of the biomarkers associated with early-lactation metabolic stress also changed during the transition from late lactation to the early dry period, but not to the same magnitude and duration previously reported in periparturient cows. Future studies should be directed toward assessing whether the magnitude and duration of biomarker expression can affect the health and well-being of cows during the early dry period.

Reduced serum vitamin D concentrations in healthy early-lactation dairy cattle.

Cattle obtain vitamin D by ingestion or cutaneous exposure to UV light. Dairy cattle diets are frequently supplemented with vitamin D to compensate for limited sun exposure or during times of increased metabolic demands, such as the periparturient period, to maintain calcium homeostasis. Whether housing and supplemental vitamin D practices supply adequate amounts of vitamin D to optimally support the transition from gestation to lactation in dairy cattle is unknown. Our objective was to determine how serum vitamin D concentrations of dairy cows change with season, age, parity, and stage of lactation. Clinically healthy cows (n = 183) from 5 commercial dairies were enrolled in the study. Serum samples were collected at dry off, within 7 d of entering the close-up group, and within 7 d after calving (calving+7). Vitamin D status was determined by measuring serum 25-hydroxyvitamin D [25(OH)D] by radioimmunoassay. We performed repeated-measures mixed-effects linear regression to determine the effects of season, age, parity, and lactation stage (dry off, close-up, and calving+7) on 25(OH)D concentrations in serum. Bivariable analysis indicated that parity, age, and season were not associated with serum 25(OH)D concentrations. Sample period affected 25(OH)D concentrations, with the highest 25(OH)D levels at dry off (99.7 ± 1.9 ng/mL) followed by close up (93.8 ± 2.1 ng/mL), with the lowest levels at calving+7 (82.6 ± 1.7 ng/mL). These data showed a large depletion of 25(OH)D in dairy cattle postpartum compared with late prepartum, although the biological significance of this change in these healthy cattle is unclear. Consumption of serum 25(OH)D by immune system functions and calcium homeostasis in early lactation likely caused the reduction in serum 25(OH)D concentrations after calving. These results suggest that determining whether serum 25(OH)D concentrations are associated with the incidence of transition period disease is an appropriate next step. Assessing the effects of enhanced vitamin D supplementation of cows in early lactation on postpartum diseases may be warranted.

Cohort-level disease prediction by extrapolation of individual-level predictions in transition dairy cattle.

Dairy cattle experience metabolic stress during the transition from late gestation to early lactation resulting in higher risk for several economically important diseases (e.g. mastitis, metritis, and ketosis). Metabolic stress is described as a physiological state composed of 3 processes: nutrient metabolism, oxidative stress, and inflammation. Current strategies for monitoring transition cow nutrient metabolism include assessment of plasma non-esterified fatty acids and beta-hydroxybutyrate concentrations around the time of calving. Although this method is effective at identifying cows with higher disease risk, there is often not enough time to implement intervention strategies to prevent health disorders from occurring around the time of calving. Previously, we published predictive models for early lactation diseases at the individual cow level at dry-off. However, it is unknown if predictive probabilities from individual-level models can be aggregated to the cohort level to predict cohort-level incidence. Therefore, our objective was to test different data aggregation methods using previously published models that represented the 3 components of metabolic stress (nutrient metabolism, oxidative stress, and inflammation). We included 277 cows from five Michigan dairy herds for this prospective cohort study. On each farm, two to four calving cohorts were formed, totaling 18 cohorts. We measured biomarker data at dry-off and followed the cows until 30 days post-parturition for cohort disease incidence, which was defined as the number of cows: 1) having one or more clinical transition disease outcome, and/or 2) having an adverse health event (abortion or death of calf or cow) within each cohort. We tested three different aggregation methods that we refer to as the p-central, p-dispersion, and p-count methods. For the p-central method, we calculated the averaged predicted probability within each cohort. For the p-dispersion method, we calculated the standard deviation of the predicted probabilities within a cohort. For the p-count method, we counted the number of cows above a specified threshold of predicted probability within each cohort. We built four sets of models: one for each aggregation method and one that included all three aggregation methods (p-combined method). We found that the p-dispersion method was the only method that produced viable predictive models. However, these models tended to overestimate incidence in cohorts with low observed counts and underestimate risk in cohorts with high observed counts.

Cohort-level disease prediction using aggregate biomarker data measured at dry-off in transition dairy cattle: A proof-of-concept study.

During the transition from late gestation to early lactation, dairy cattle are at increased risk for disease. Herd-level monitoring for disease risk involves evaluating multiple factors, including food intake, cow density, and biomarkers of nutrient metabolism. Biomarkers that are measured include non-esterified fatty acids (NEFA) and beta-hydroxybutyrate (BHB), which are usually measured in a subset of the herd (i.e. cohort). If a certain proportion of cows in the cohort are above a specific threshold for a biomarker, the cohort is considered at high risk of disease. Few previous studies have investigated other methods to aggregate individual cow-level data to the cohort level. We designed a proof-of-concept study to determine if biomarker aggregation methods may be useful to predict cohort incidence of adverse health events including 1) clinical diseases: mastitis, metritis, retained placenta, ketosis, lameness, pneumonia, milk fever, displaced abomasum, 2) and abortion or death of the calf or the cow. The study design was a prospective cohort study that used cows (N = 277) from five Michigan commercial dairy herds. Multiple cohorts of cows (two to four cohorts per farm, 18 total) were enrolled that shared the same dry-off date. We tested three different methods (central, dispersion, and count) to aggregate individual cow data (i.e. biomarkers and covariates) measured at dry-off. The central method consisted of calculating the average value of each variable within a cohort, and the dispersion method involved taking the standard deviation or mean absolute deviation about the median of each variable within a cohort. The count method consisting of counting the number of cows above a specific threshold for each variable within a cohort. We used best subsets selection to select a bouquet of candidate models for each aggregation method and averaged the predictions over the model set. We built 4 sets of Poisson regression models: one for each aggregation method and a combined model that included all three methods. We evaluated the models based on goodness-of-fit, model calibration using scoring rules, and comparison of observed versus predicted counts. The central and the combined method produced models that had good fit and model calibration. These results indicate that it may be possible to use aggregate measures to predict cohort disease incidence as early as dry-off. The next step is to test biomarker aggregation methods in studies with larger sample sizes.

Predictive models for early lactation diseases in transition dairy cattle at dry-off.

During the transition period, dairy cattle undergo tremendous metabolic and physiological changes to prepare for milk synthesis and secretion. Failure to sufficiently regulate these changes may lead to metabolic stress, which increases risk of transition diseases. Metabolic stress is defined as a physiological state consisting of 3 components: aberrant nutrient metabolism, oxidative stress, and inflammation. Current monitoring methods to detect cows experiencing metabolic stress involve measuring biomarkers for nutrient metabolism. However, these biomarkers, including non-esterified fatty acids, beta-hydroxybutyrate, and calcium are typically measured a few weeks before to a few days after calving. This is a retroactive approach, because there is little time to integrate interventions that remediate metabolic stress in the current cohort. Our objective was to determine if biomarkers of metabolic stress measured at dry-off are predictive of transition disease risk. We designed a prospective cohort study carried out on 5 Michigan dairy farms (N = 277 cows). We followed cows from dry-off to 30 days post-calving. Diseases and adverse outcomes were grouped in an aggregate outcome that included mastitis, metritis, retained placenta, ketosis, lameness, pneumonia, milk fever, displaced abomasum, abortion, and death of the calf or the cow. We used best subsets selection to select candidate models for four different sets of models: one set for each component of metabolic stress (nutrient metabolism, oxidative stress, and inflammation), and a combined model that included all 3 components. We used model averaging to obtain averaged predicted probabilities across each model set. We hypothesized that the averaged predictions from the combined model set with all 3 components of metabolic stress would be more effective at predicting disease than each individual component model set. The area under the curve estimated using receiver operator characteristic curves for the combined model set (0.93; 95% confidence interval [CI] = 0.90-0.96) was significantly higher compared with averaged predictions from the inflammation (0.87; 95% CI = 0.83-0.91), oxidative stress (0.78; 95% CI = 0.72-0.84), and nutrient metabolism (0.73; 95% CI = 0.67-0.79) model sets (p < 0.05). Our results indicate that it may be possible to detect cattle at risk for some transition diseases as early as dry-off. This has important implications for disease prevention, as earlier identification of cows at risk of health disorders will allow for earlier implementation of intervention strategies. A limitation of the current study is that we did not perform external validation. Future validation studies are needed to confirm our findings.