Validation of milk mid-infrared spectroscopy for predicting the metabolic status of lactating dairy cows in Australia

Increased concentrations of some serum biomarkers are known to be associated with impaired health of dairy cows. Therefore, being able to predict these biomarkers, especially in the early stage of lactation, would enable preventive management decision. Some health biomarkers may also be used as phenotypes for genetic improvement for improved animal health. In this study, we validated the accuracy and robustness of models for predicting serum concentrations of β-hydroxybutyrate (BHB), fatty acids, and urea nitrogen, using milk mid-infrared (MIR) spectroscopy. The data included 3,262 blood samples of 3,027 lactating Holstein-Friesian cows from 19 dairy herds in Southeastern Australia, collected in the period from July 2017 to April 2020. The models were developed using partial least squares regression and were validated using 10-fold random cross-validation, herd-year by herd-year external validation, and year by year validation. The coefficients of determination (R²) for prediction of serum BHB, fatty acids, and urea obtained through random cross-validation were 0.60, 0.42, and 0.87, respectively. For the herd-year by herd-year external validation, the prediction accuracies held up comparatively well, with R² values of 0.49, 0.33, and 0.67 for of serum BHB, fatty acids, and urea, respectively. When the models were developed using data from a single year to predict data collected in future years, the R² remained comparable, however, the root mean squared errors increased substantially (4–10 times larger than compared with that of herd-year by herd-year external validation) which could be due to machine differences in spectral response, the change in spectral response of individual machines over time, or other differences associated with farm management between seasons. In conclusion, the mid-infrared equations for predicting serum BHB, fatty acids, and urea have been validated. The prediction equations could be used to help farmers detect cows with metabolic disorders in early lactation in addition to generating novel phenotypes for genetic improvement purposes.     

Prediction of blood metabolites from milk mid-infrared spectra in early-lactation cows

Dairy cows commonly experience an unbalanced energy status in early lactation, and this condition can lead to the onset of several metabolic disorders. Blood metabolic profile testing is a valid tool to monitor and detect the most common early lactation disorders, but blood sampling and analysis are time-consuming and expensive, and the procedure is invasive and stressful for the cows. Mid-infrared (MIR) spectroscopy is routinely used to analyze milk composition, being a cost-effective and nondestructive method. The present study aimed to assess the feasibility of using routine milk MIR spectra for the prediction of main blood metabolites in dairy cows, and to investigate associations between measured blood metabolites and milk traits. Twenty herds of Holstein Friesian, Brown Swiss, or Simmental cows located in Northeast Italy were visited 1 to 4 times between December 2017 and June 2018, and blood and milk samples were collected from all lactating cows within 35 d in milk. Concentrations of main blood metabolites and milk MIR spectra were recorded from 295 blood and milk samples and used to develop prediction models for blood metabolic traits through backward interval partial least squares analysis. Blood β-hydroxybutyrate (BHB), urea, and nonesterified fatty acids were the most predictable traits, with coefficients of determination of 0.63, 0.58, and 0.52, respectively. On the contrary, predictive performance for blood glucose, triglycerides, cholesterol, glutamic oxaloacetic transaminase, and glutamic pyruvic transaminase were not accurate. Associations of blood BHB and urea with their respective contents in milk were moderate to strong, whereas all other correlations were weak. Predicted blood BHB showed an improved performance in detecting cows with hyperketonemia (blood BHB ≥ 1.2 mmol/L), compared with commercial calibration equation for milk BHB. Results highlighted the opportunity of using milk MIR spectra to predict blood metabolites and thus to collect routine information on the metabolic status of early-lactation cows at a population level.     

The use of milk mid-infrared spectroscopy to improve genomic prediction accuracy of serum biomarkers

Breeding objectives in the dairy industry have shifted from being solely focused on production to including fertility, animal health, and environmental impact. Increased serum concentrations of candidate biomarkers of health and fertility, such as β-hydroxybutyric acid (BHB), fatty acids, and urea are difficult and costly to measure, and thus limit the number of records. Accurate genomic prediction requires a large reference population. The inclusion of milk mid-infrared (MIR) spectroscopic predictions of biomarkers may increase genomic prediction accuracy of these traits. Our objectives were to (1) estimate the heritability of, and genetic correlations between, selected serum biomarkers and their respective MIR predictions, and (2) evaluate genomic prediction accuracies of either only measured serum traits, or serum traits plus MIR-predicted traits. The MIR-predicted traits were either fitted in a single trait model, assuming the measured trait and predicted trait were the same trait, or in a multitrait model, where measured and predicted trait were assumed to be correlated traits. We performed all analyses using relationship matrices constructed from pedigree (A matrix), genotypes (G matrix), or both pedigree and genotypes (H matrix). Our data set comprised up to 2,198 and 9,657 Holstein cows with records for serum biomarkers and MIR-predicted traits, respectively. Heritabilities of measured serum traits ranged from 0.04 to 0.07 for BHB, from 0.13 to 0.21 for fatty acids, and from 0.10 to 0.12 for urea. Heritabilities for MIR-predicted traits were not significantly different from those for the measured traits. Genetic correlations between measured traits and MIR-predicted traits were close to 1 for urea. For BHB and fatty acids, genetic correlations were lower and had large standard errors. The inclusion of MIR predicted urea substantially increased prediction accuracy for urea. For BHB, including MIR-predicted BHB reduced the genomic prediction accuracy, whereas for fatty acids, prediction accuracies were similar with either measured fatty acids, MIR-predicted fatty acids, or both. The high genetic correlation between urea and MIR-predicted urea, in combination with the increased prediction accuracy, demonstrated the potential of using MIR-predicted urea for genomic prediction of urea. For BHB and fatty acids, further studies with larger data sets are required to obtain more accurate estimates of genetic correlations.     

Diagnostic milk biomarkers for predicting the metabolic health status of dairy cattle during early lactation

Data on metabolic profiles of blood sampled at d 3, 6, 9, and 21 in lactation from 117 lactations (99 cows) were used for unsupervised k-means clustering. Blood metabolic parameters included β-hydroxybutyrate (BHB), nonesterified fatty acids, glucose, insulin-like growth factor-1 (IGF-1) and insulin. Clustering relied on the average and range of the 5 blood parameters of all 4 sampling days. The clusters were labeled as imbalanced (n = 42) and balanced (n = 72) metabolic status based on the values of the blood parameters. Various random forest models were built to predict the metabolic cluster of cows during early lactation from the milk composition. All the models were evaluated using a leave-group-out cross-validation, meaning data from a single cow were always present in either train or test data to avoid any data leakage. Features were either milk fatty acids (MFA) determined by gas chromatography (MFA [GC]) or features that could be determined during a routine dairy herd improvement (DHI) analysis, such as concentration of fat, protein, lactose, fat/protein ratio, urea, and somatic cell count (determined and reported routinely in DHI registrations), either or not in combination with MFA and BHB determined by mid-infrared (MIR), denoted as MFA [MIR] and BHB [MIR], respectively, which are routinely analyzed but not routinely reported in DHI registrations yet. Models solely based on fat, protein, lactose, fat/protein ratio, urea and somatic cell count (i.e., DHI model) were characterized by the lowest predictive performance [area under the receiver operating characteristic curve (AUC_ROC) = 0.69]. The combination of the features of the DHI model with BHB [MIR] and MFA [MIR] powerfully increased the predictive performance (AUC_ROC = 0.81). The model based on the detailed MFA profile determined by GC analysis did not outperform (AUC_ROC = 0.81) the model using the DHI-features in combination with BHB [MIR] and MFA [MIR]. Predictions solely based on samples at d 3 were characterized by lower performance (AUC_ROC DHI + BHB [MIR] + MFA [MIR] model at d 3: 0.75; AUC_ROC MFA [GC] model at d 3: 0.73). High predictive performance was found using samples from d 9 and 21. To conclude, overall, the DHI + BHB [MIR] + MFA [MIR] model allowed to predict metabolic status during early lactation. Accordingly, these parameters show potential for routine prediction of metabolic status.     

Prediction of metabolic clusters in early-lactation dairy cows using models based on milk biomarkers

The aim of this study was to describe metabolism of early-lactation dairy cows by clustering cows based on glucose, insulin-like growth factor I (IGF-I), free fatty acid, and β-hydroxybutyrate (BHB) using the k-means method. Predictive models for metabolic clusters were created and validated using 3 sets of milk biomarkers (milk metabolites and enzymes, glycans on the immunogamma globulin fraction of milk, and Fourier-transform mid-infrared spectra of milk). Metabolic clusters are used to identify dairy cows with a balanced or imbalanced metabolic profile. Around 14 and 35 d in milk, serum or plasma concentrations of BHB, free fatty acids, glucose, and IGF-I were determined. Cows with a favorable metabolic profile were grouped together in what was referred to as the “balanced” group (n = 43) and were compared with cows in what was referred to as the “other balanced” group (n = 64). Cows with an unfavorable metabolic profile were grouped in what was referred to as the “imbalanced” group (n = 19) and compared with cows in what was referred to as the “other imbalanced” group (n = 88). Glucose and IGF-I were higher in balanced compared with other balanced cows. Free fatty acids and BHB were lower in balanced compared with other balanced cows. Glucose and IGF-I were lower in imbalanced compared with other imbalanced cows. Free fatty acids and BHB were higher in imbalanced cows. Metabolic clusters were related to production parameters. There was a trend for a higher daily increase in fat- and protein-corrected milk yield in balanced cows, whereas that of imbalanced cows was higher. Dry matter intake and the daily increase in dry matter intake were higher in balanced cows and lower in imbalanced cows. Energy balance was continuously higher in balanced cows and lower in imbalanced cows. Weekly or twice-weekly milk samples were taken and milk metabolites and enzymes (milk glucose, glucose-6-phosphate, BHB, lactate dehydrogenase, N-acetyl-β-d-glucosaminidase, isocitrate), immunogamma globulin glycans (19 peaks), and Fourier-transform mid-infrared spectra (1,060 wavelengths reduced to 15 principal components) were determined. Milk biomarkers with or without additional cow information (days in milk, parity, milk yield features) were used to create predictive models for the metabolic clusters. Accuracy for prediction of balanced (80%) and imbalanced (88%) cows was highest using milk metabolites and enzymes combined with days in milk and parity. The results and models of the present study are part of the GplusE project and identify novel milk-based phenotypes that may be used as predictors for metabolic and performance traits in early-lactation dairy cows.     

Relationships between body condition,body condition loss,and serum metabolites during the transition period in primiparous and multiparous cows

In the transition period from late gestation to early lactation, dairy cows undergo tremendous metabolic changes. Insulin is a relevant antilipolytic factor. Decreasing serum concentrations of insulin and glucose, increasing serum concentrations of nonesterified fatty acids (NEFA) and β-hydroxybutyrate (BHB), and changes in body condition score (BCS) reflect the negative energy balance around calving. This study investigated peripartum metabolic adaptation in 359 primiparous and 235 multiparous German Holstein cows from a commercial dairy herd under field conditions. Body condition score was recorded and blood samples were taken 10 to 1 d prepartum, 2 to 4 d postpartum, and 12 to 20 d postpartum. Generalized mixed models and generalized estimation equations were applied to assess associations between prepartum BCS; BCS changes during the transition period; insulin, glucose, NEFA, and BHB serum concentrations; and milk yield, which was taken from an electronic milk meter from d 6 of lactation. Serum insulin concentrations of multiparous postpartum cows were lower compared with prepartum, and compared with primiparous cows. In general, primiparous cows had lower postpartum NEFA and BHB concentrations than multiparous cows. In primiparous cows, we identified a positive association between prepartum BCS and prepartum serum insulin concentration. Prepartum obese multiparous cows, but not primiparous cows, were characterized by higher postpartum serum NEFA and BHB concentrations and lower milk yield than other cows in the same parity class. Primiparous cows with a smaller degree of BCS loss during the transition period had higher postpartum insulin and lower NEFA concentrations and lower milk yield than other primiparous cows. In conclusion, primiparous cows had less lipolysis and lower milk yield than multiparous cows, associated with higher insulin concentrations. Avoiding high body condition loss during the transition period is a main factor in preventing peripartal metabolic imbalances of glucose and fat metabolism.     

Effects of shortening the close-up period length coupled with increased supply of metabolizable protein on performance and metabolic status of multiparous Holstein cows

This experiment was conducted to compare conventional (CON; 21 d) and shortened (SH; 10 d) close-up period, and evaluate the effect of shortened close-up period combined with feeding different metabolizable protein (MP) levels on dry matter (DM) intake, metabolic status, and performance of dairy cows. Forty-eight multiparous Holstein cows with similar parity, body weight (BW), and previous lactation milk yield were divided into 2 groups. The first group (n = 24) received the far-off diet from ?60 to ?21 d (CON), and the second group (n = 24) received same far-off diet from ?60 to ?10 d (SH) relative to expected parturition. Cows were then moved to individual stalls and randomly allocated to 1 of 3 close-up diets: low MP diet (LMP; MP = 79 g/kg of DM), medium MP diet (MMP; MP = 101 g/kg of DM), or high MP diet (HMP; MP = 118 g/kg of DM). Treatments were used in a 2 × 3 factorial arrangement with 2 lengths of close-up period (CON and SH) and 3 levels of MP (LMP, MMP, and HMP). All diets were fed for ad libitum intake during the close-up period. After calving, all cows received the same fresh cow diet. We found no interaction between close-up period length and MP levels for traits, except for postpartum serum fatty acids and β-hydroxybutyrate (BHB). The concentrations of postpartum serum fatty acids and BHB were higher on LMP than MMP and HMP diets in SH group. The cows of the SH group tended to produce less colostrum in the first milking than cows in CON group. The length of close-up period did not affect pre- and postpartum DM intake or energy balance of cows during the last week of prepartum, but cows of the CON group had greater BW changes during the last 3 wk before parturition than cows in SH group. Cows fed MMP and HMP diets consumed 1.2 and 1 kg more DM than for those fed LMP prepartum, respectively. The concentrations of prepartum BHB and Ca were higher for SH cows than CON group cows. Except for blood urea N concentration, no other blood metabolite in prepartum was affected by dietary MP. We found no effects of close-up period length or MP levels in the close-up diet on urinary pH, purine derivative excretion, and microbial N flow. Postpartum, milk yield was not affected by close-up period length, but cows in CON group tended to have higher 4% fat-corrected milk yield, had higher milk fat content and yield, had greater BW and body condition score loss, and higher energy negative balance than cows in the SH group. Cows fed MMP diet ate 1.8 kg more DM and yielded 3.37 kg more milk than those fed the LMP diet. Milk fat, protein, and lactose content, milk urea N, and somatic cell count were not affected by MP levels, but the yield of milk protein and lactose were higher on MMP diet than on LMP diet. Concentrations of postpartum serum fatty acids and BHB were decreased by shortening the close-up period length, but glucose, cholesterol, and triglyceride were similar between close-up groups. During the postpartum period, serum fatty acids, BHB, aminotransferase, and Ca concentrations were decreased by increasing the MP levels in the close-up diet. It appears from this data set that multiparous cows will benefit from a shortened close-up period, and feeding a moderate MP diet could improve DM intake, milk yield, and metabolic status of periparturient dairy cows.     

Classifying the fertility of dairy cows using milk mid-infrared spectroscopy

The objective of this study was to investigate the potential of milk mid-infrared (MIR) spectroscopy, MIR-derived traits including milk composition, milk fatty acids, and blood metabolic profiles (fatty acids, β-hydroxybutyrate, and urea), and other on-farm data for discriminating cows of good versus poor likelihood of conception to first insemination (i.e., pregnant vs. open). A total of 6,488 spectral and milk production records of 2,987 cows from 19 commercial dairy herds across 3 Australian states were used. Seven models, comprising different explanatory variables, were examined. Model 1 included milk production; concentrations of fat, protein, and lactose; somatic cell count; age at calving; days in milk at herd test; and days from calving to insemination. Model 2 included, in addition to the variables in model 1, milk fatty acids and blood metabolic profiles. The MIR spectrum collected before first insemination was added to model 2 to form model 3. Fat, protein, and lactose percentages, milk fatty acids, and blood metabolic profiles were removed from model 3 to create model 4. Model 5 and model 6 comprised model 4 and either fertility genomic estimated breeding value or principal components obtained from a genomic relationship matrix derived using animal genotypes, respectively. In model 7, all previously described sources of information, but not MIR-derived traits, were used. The models were developed using partial least squares discriminant analysis. The performance of each model was evaluated in 2 ways: 10-fold random cross-validation and herd-by-herd external validation. The accuracy measures were sensitivity (i.e., the proportion of pregnant cows that were correctly classified), specificity (i.e., the proportion of open cows that were correctly classified), and area under the curve (AUC) for the receiver operating curve. The results showed that in all models, prediction accuracy obtained through 10-fold random cross-validation was higher than that of herd-by-herd external validation, with the difference in AUC ranging between 0.01 and 0.09. In the herd-by-herd external validation, using basic on-farm information (model 1) was not sufficient to classify good- and poor-fertility cows; the sensitivity, specificity, and AUC were around 0.66. Compared with model 1, adding milk fatty acids and blood metabolic profiles (model 2) increased the sensitivity, specificity, and AUC by 0.01, 0.02, and 0.02 unit, respectively (i.e., 0.65, 0.63, and 0.678). Incorporating MIR spectra into model 2 resulted in sensitivity, specificity, and AUC values of 0.73, 0.63, and 0.72, respectively (model 3). The comparable prediction accuracies observed for models 3 and 4 mean that useful information from MIR-derived traits is already included in the spectra. Adding the fertility genomic estimated breeding value and animal genotypes (model 7) produced the highest prediction accuracy, with sensitivity, specificity, and AUC values of 0.75, 0.66, and 0.75, respectively. However, removing either the fertility estimated breeding value or animal genotype from model 7 resulted in a reduction of the prediction accuracy of only 0.01 and 0.02, respectively. In conclusion, this study indicates that MIR and other on-farm data could be used to classify cows of good and poor likelihood of conception with promising accuracy.     

Associations between the detailed milk mineral profile,milk composition,and metabolic status in Holstein cows

The causes of variation in the milk mineral profile of dairy cattle during the first phase of lactation were studied under the hypothesis that the milk mineral profile partially reflects the animals' metabolic status. Correlations between the minerals and the main milk constituents (i.e., protein, fat, and lactose percentages), and their associations with the cows' metabolic status indicators were explored. The metabolic status indicators (MET) that we used were blood energy-protein metabolites [nonesterified fatty acids, β-hydroxybutyrate (BHB), glucose, cholesterol, creatinine, and urea], and liver ultrasound measurements (predicted triacylglycerol liver content, portal vein area, portal vein diameter and liver depth). Milk and blood samples, and ultrasound measurements were taken from 295 Holstein cows belonging to 2 herds and in the first 120 d in milk (DIM). Milk mineral contents were determined by ICP-OES; these were considered the response variable and analyzed through a mixed model which included DIM, parity, milk yield, and MET as fixed effects, and the herd/date as a random effect. The MET traits were divided in tertiles. The results showed that milk protein was positively associated with body condition score (BCS) and glucose, and negatively associated with BHB blood content; milk fat was positively associated with BHB content; milk lactose was positively associated with BCS; and Ca, P, K and S were the minerals with the greatest number of associations with the cows' energy indicators, particularly BCS, predicted triacylglycerol liver content, glucose, BHB and urea. We conclude that the protein, fat, lactose, and mineral contents of milk partially reflect the metabolic adaptation of cows during lactation and within 120 DIM. Variations in the milk mineral profile were consistent with changes in the major milk constituents and the metabolic status of cows.     

Prediction of metabolic status of dairy cows in early lactation with on-farm cow data and machine learning algorithms

Metabolic status of dairy cows in early lactation can be evaluated using the concentrations of plasma β-hydroxybutyrate (BHB), free fatty acids (FFA), glucose, insulin, and insulin-like growth factor 1 (IGF-1). These plasma metabolites and metabolic hormones, however, are difficult to measure on farm. Instead, easily obtained on-farm cow data, such as milk production traits, have the potential to predict metabolic status. Here we aimed (1) to investigate whether metabolic status of individual cows in early lactation could be clustered based on their plasma values and (2) to evaluate machine learning algorithms to predict metabolic status using on-farm cow data. Through lactation wk 1 to 7, plasma metabolites and metabolic hormones of 334 cows were measured weekly and used to cluster each cow into 1 of 3 clusters per week. The cluster with the greatest plasma BHB and FFA and the lowest plasma glucose, insulin, and IGF-1 was defined as poor metabolic status; the cluster with the lowest plasma BHB and FFA and the greatest plasma glucose, insulin, and IGF-1 was defined as good metabolic status; and the intermediate cluster was defined as average metabolic status. Most dairy cows were classified as having average or good metabolic status, and a limited number of cows had poor metabolic status (10–50 cows per lactation week). On-farm cow data, including dry period length, parity, milk production traits, and body weight, were used to predict good or average metabolic status with 8 machine learning algorithms. Random Forest (error rate ranging from 12.4 to 22.6%) and Support Vector Machine (SVM; error rate ranging from 12.4 to 20.9%) were the top 2 best-performing algorithms to predict metabolic status using on-farm cow data. Random Forest had a higher sensitivity (range: 67.8–82.9% during wk 1 to 7) and negative predictive value (range: 89.5–93.8%) but lower specificity (range: 76.7–88.5%) and positive predictive value (range: 58.1–78.4%) than SVM. In Random Forest, milk yield, fat yield, protein percentage, and lactose yield had important roles in prediction, but their rank of importance differed across lactation weeks. In conclusion, dairy cows could be clustered for metabolic status based on plasma metabolites and metabolic hormones. Moreover, on-farm cow data can predict cows in good or average metabolic status, with Random Forest and SVM performing best of all algorithms.     

Effect of hyperketonemia on the diurnal patterns of energy-related blood metabolites in early-lactation dairy cows

Most dairy cows experience a period of energy deficit in early lactation, resulting in increased plasma concentrations of nonesterified fatty acids (NEFA) and β-hydroxybutyrate (BHB). Our objectives were to determine (1) the diurnal variation in plasma BHB and NEFA, (2) the correlation between plasma NEFA and BHB when accounting for diurnal changes, and (3) the effect of hyperketonemia (HYK) on the diurnal pattern of blood metabolites. Jugular catheters were placed in 28 multiparous Holstein cows between 3 and 9 days in milk, and blood samples were collected every 2 h for 96 h. Cows were retrospectively classified as HYK positive (HYK; n = 13) if they had plasma BHB concentrations ≥1.2 mmol/L for ≥3 study days, or HYK negative (non-HYK; n = 15) if they had plasma BHB concentrations ≥1.2 mmol/L for ≤2 study days. Generalized linear mixed models were used to analyze concentrations of analytes over time and differences in metabolites between HYK groups. The correlation between total plasma NEFA and BHB was analyzed by calculating the area under the curve for plasma NEFA and BHB for all cows. Plasma NEFA reached a peak approximately 2 h before morning feed delivery, falling to a nadir in the late evening. Plasma BHB was at a nadir at the time of morning feed delivery, peaking 4 h later. We observed a strong positive correlation between daily plasma NEFA and BHB. Additionally, HYK cows had greater concentrations of plasma NEFA and BHB than non-HYK cows. The HYK cows also experienced a greater magnitude of change in BHB throughout the day than the non-HYK cows. Our results suggest that the time relative to feeding should be considered when analyzing plasma metabolites, as classification of energy status may change throughout a day.     

The relationships between early lactation energy status indicators and endocrine fertility traits in dairy cows

The relationships between dairy cow milk-based energy status (ES) indicators and fertility traits were studied during periods 8 to 21, 22 to 35, 36 to 49, and 50 to 63 d in milk. Commencement of luteal activity (C-LA) and interval from calving to the first heat (CFH), based on frequent measurements of progesterone by the management tool Herd Navigator (DeLaval), were used as fertility traits. Energy status indicator traits were milk β-hydroxybutyrate (BHB) concentration provided by Herd Navigator and milk fat:protein ratio, concentration of C18:1 cis-9, the ratio of fatty acids (FA) C18:1 cis-9 and C10:0 in test-day milk samples, and predicted plasma concentration of nonesterified fatty acids (NEFA) on test days. Plasma NEFA predictions were based either directly on milk mid-infrared spectra (MIR) or on milk fatty acids based on MIR spectra (NEFA_mir and NEFA_fa, respectively). The average (standard deviation) C-LA was 39.3 (±16.6) days, and the average CFH was 50.7 (±17.2) days. The correlations between fertility traits and ES indicators tended to be higher for multiparous (r < 0.28) than for primiparous (r < 0.16) cows. All correlations were lower in the last period than in the other periods. In period 1, correlations of C-LA with NEFA_fa and BHB, respectively, were 0.15 and 0.14 for primiparous and 0.26 and 0.22 for multiparous cows. The associations between fertility traits and ES indicators indicated that negative ES during the first weeks postpartum may delay the onset of luteal activity. Milk FPR was not as good an indicator for cow ES as other indicators. According to these findings, predictions of plasma NEFA and milk FA based on milk MIR spectra of routine test-day samples and the frequent measurement of milk BHB by Herd Navigator gave equally good predictions of cow ES during the first weeks of lactation. Our results indicate that routinely measured milk traits can be used for ES evaluation in early lactation.     

Short communication: Validation of a test-day milk test for β-hydroxybutyrate for identifying cows with hyperketonemia

Subclinical ketosis is a common metabolic disorder affecting dairy cattle that results in a greater risk for the development of subsequent metabolic and infectious disease. Canwest Dairy Herd Improvement (DHI; Guelph, ON, Canada) has begun to use an infrared test (MilkoScan FT600, Foss Analytical A/S, Hillerød, Denmark) applied to metered composite milk samples to detect β-hydroxybutyrate (BHB) levels as a herd surveillance test for hyperketonemia. However, the test has not been compared with the gold standard, serum BHB as determined in a reference diagnostic laboratory. The objective of this cross-sectional diagnostic accuracy study was to validate the DHI milk BHB test to identify cows with hyperketonemia as determined by quantification of BHB in serum. A total of 316 cows from 17 dairy herds in southwestern Ontario had a milk and blood sample taken. Milk was collected at a routine DHI test, and blood from the same cow was sampled within 24 h of the milk test. The BHB concentration in milk was determined using the DHI milk BHB test, and serum was sent to the Animal Health Laboratory at the University of Guelph (Guelph, ON, Canada). A nonparametric receiver operating characteristic curve was generated to compare DHI milk BHB concentrations with serum BHB concentrations. Overall, a total of 34 cows (11%) had a level of serum BHB ≥1.2 mmol/L. The concentration of DHI milk BHB was moderately correlated with the concentration of serum BHB, yielding a coefficient of determination value of 0.61. The optimal cut point for determining hyperketonemia (≥1.2 mmol/L) on the DHI milk BHB test was ≥0.14 mmol/L, yielding a sensitivity of 81% and specificity of 92%. The performance of the DHI milk BHB test varied depending on the days in milk (DIM) of the cows tested, with a higher specificity being found in cows that were ≤25 DIM compared with cows tested >25 DIM. If the herd-level prevalence of hyperketonemia was ≥14%, the DHI milk BHB test had an improved sensitivity when compared with a herd-level prevalence of <14%. This study demonstrates that the DHI milk BHB test is a reliable measure for evaluating hyperketonemia using routine DHI milk samples and could be used as a herd-level monitoring tool for ketosis when evaluating nutritional management or preventative medicine strategies.     

Short communication: Circulating serotonin is related to the metabolic status and lactational performance at the onset of lactation in dairy cows

Serotonin (5-hydroxytryptamine, 5-HT) affects many physiological functions because it is involved in glucose and lipid metabolism, calcium homeostasis, and regulation of lactation in dairy cows. This study aimed to examine physiological differences in serum 5-HT concentrations (high vs. low) and their association with metabolic status and milk production at the onset of lactation. Twelve multiparous Holstein dairy cows were milked within 4 h of calving, and blood and milk samples were collected at the first 6 subsequent milkings after parturition and at the evening milkings on d 5, 8, 10, and 14. Cows were retrospectively divided into 2 groups (6 cows/group): low serum 5-HT (LSS) and high serum 5-HT (HSS) according to their calculated areas under the curve (AUC) for serum 5-HT for the entire experimental period (cut-off: 46,000 ng/mL × 324 h). Concentrations of 5-HT, free fatty acids (FFA), β-hydroxybutyrate (BHB), glucose, calcium, and IGF-1 were measured in blood. Milk was analyzed for fat, protein, lactose, and 5-HT concentrations. Milk yield was recorded at each milking and energy-corrected milk yield was calculated. Serum 5-HT concentrations were higher in HSS than in LSS [AUC (ng/mL × 324 h): 57,830 ± 4,810 vs. 25,005 ± 5,930]. The amount of energy-corrected milk was lower in HSS than in LSS. The HSS group produced less colostrum and had decreased milk yield, specifically during the first 6 milkings. Concentrations of FFA, BHB, and glucose in plasma did not differ between groups. Concentrations of IGF-1 in serum were elevated in HSS compared with LSS throughout the experiment. Total circulating calcium concentrations in serum tended to be higher in HSS than in LSS. Milk fat and protein yields were decreased in HSS compared with LSS. Milk 5-HT decreased overall during the experimental period, with LSS maintaining higher 5-HT concentrations than HSS until d 14 of lactation. In conclusion, cows with high serum 5-HT concentrations showed a reduced metabolic load at the onset of lactation, concomitantly lower milk yield, and a reduced energy output via milk.     

Prepartum intake, postpartum induction of ketosis, and periparturient disorders affect the metabolic status of dairy cows

Nutritional management during the dry period may affect susceptibility of cows to metabolic and infectious diseases during the periparturient period. Thirty-five multiparous Holstein cows were used to determine the effect of prepartum intake, postpartum induction of ketosis, and periparturient disorders on metabolic status. Cows were fed a diet from dry-off to parturition at either ad libitum intake or restricted intake [RI; 80% of calculated net energy for lactation (NE_L) requirement]. After parturition, all cows were fed a lactation diet. At 4 d in milk (DIM), cows underwent a physical examination and were classified as healthy or having at least one periparturient disorder (PD). Healthy cows were assigned to the control (n = 6) group or the ketosis induction (KI; n = 9) group. Cows with PD were assigned to the PD control (PDC; n = 17) group. Cows in the control and PDC groups were fed for ad libitum intake. Cows in the KI group were fed at 50% of their intake on 4 DIM from 5 to 14 DIM or until signs of clinical ketosis were observed; then, cows were returned to ad libitum intake. During the dry period, ad libitum cows ate more than RI cows; the difference in intake resulted in ad libitum cows that were in positive energy balance (142% of NE_L requirement) and RI cows that were in negative energy balance (85% of NE_L requirement). Prepartum intake resulted in changes in serum metabolites consistent with plane of nutrition and energy balance. Prepartum intake had no effect on postpartum intake, serum metabolites, or milk yield, but total lipid content of liver at 1 d postpartum was greater for ad libitum cows than for RI cows. The PD cows had lower intake and milk yield during the first 4 DIM than did healthy cows. During the ketosis induction period, KI cows had lower intake, milk yield, and serum glucose concentration but higher concentrations of nonesterified fatty acids and β-hydroxybutyrate in serum as well as total lipid and triacylglycerol in liver than did control cows. Cows with PD had only modest alterations in metabolic variables in blood and liver compared with healthy cows. The negative effects of PD and KI on metabolic status and milk yield were negligible by 42 DIM, although cows with PD had lower body condition score and BW. Prepartum intake did not affect postpartum metabolic status or milk yield. Periparturient disorders and induction of ketosis negatively affected metabolic status and milk yield during the first 14 DIM.     

Milk metabolites and fatty acids as noninvasive biomarkers of metabolic status and energy balance in early-lactation cows

The objective was to study the effects of week of lactation (WOL) and experimental nutrient restriction on concentrations of selected milk metabolites and fatty acids (FA), and assess their potential as biomarkers of energy status in early-lactation cows. To study WOL effects, 17 multiparous Holstein cows were phenotyped from calving until 7 WOL while allowed ad libitum intake of a lactation diet. Further, to study the effects of nutrient restriction, 8 of these cows received a diet containing 48% straw (high-straw) for 4 d starting at 24 ± 3 days in milk (mean ± SD), and 8 cows maintained on the lactation diet were sampled to serve as controls. Blood and milk samples were collected weekly for the WOL data set, and daily from d ?1 to 3 of nutrient restriction (or control) for the nutritional challenge data set. Milk β-hydroxybutyrate (BHB), isocitrate, glucose, glucose-6-phosphate (glucose-6P), galactose, glutamate, creatinine, uric acid, and N-acetyl-β-d-glucosaminidase activity (NAGase) were analyzed in p.m. and a.m. samples, and milk FA were analyzed in pooled p.m. and a.m. samples. Average energy balance (EB) per day ranged from ?27 MJ/d to neutral when cows received the lactation total mixed ration, and from ?109 to ?87 ± 7 MJ/d for high-straw (least squares means ± standard error of the mean). Plasma nonesterified FA concentration was 1.67 ± 0.13 mM and BHB was 2.96 ± 0.39 mM on the d 3 of high-straw (least squares means ± standard error of the mean). Milk concentrations of BHB, glucose, glucose-6P, glutamate, and uric acid differed significantly between p.m. and a.m. milkings. Milk isocitrate, glucose-6P, creatinine, and NAGase decreased, whereas milk glucose and galactose increased with WOL. Changes in milk BHB, isocitrate, glucose, glucose-6P, and creatinine were concordant during early lactation and in response to nutrient restriction. Milk galactose and NAGase were modulated by WOL only, whereas glutamate and uric acid concentrations responded to nutrient restriction only. The high-straw increased milk concentrations of FA potentially mobilized from adipose tissue (e.g., C18:0 and cis-9 C18:1 and sum of odd- and branched-chain FA (OBCFA) with carbon chain greater than 16; ∑ OBCFA >C16), and decreased concentrations of FA synthesized de novo by the mammary gland (e.g., sum of FA with 6 to 15 carbons; ∑ C6:0 to C15:0). Similar observations were made during early lactation. Plasma nonesterified FA concentrations had the best single linear regression with EB (R² = 0.62). Milk isocitrate, Σ C6:0 to C15:0. and cis-9 C18:1 had the best single linear regressions with EB (R² ≥ 0.44). Milk BHB, isocitrate, galactose, glutamate, and creatinine explained up to 64% of the EB variation observed in the current study using multiple linear regression. Milk concentrations of ∑ C6:0 to C15:0, C18:0, cis-9 C18:1, and ∑ OBCFA >C16 presented some of the best correlations and regressions with other indicators of metabolic status, lipomobilization, and EB, and their responses were concordant during early lactation and during experimental nutrient restriction. Metabolites and FA secreted in milk may serve as noninvasive indicators of metabolic status and EB of early-lactation cows.     

Effects of bovine somatotropin on physiologic responses of lactating Holstein and Jersey cows during hot, humid weather

Thirty-one lactating Holstein and Jersey cows were used to determine the effects of daily injections of 0 or 20 mg of recombinant bST on physiologic responses during hot, humid weather. Body temperature was determined by measuring milk temperature at each milking. Jugular blood was sampled for serum analysis of selected hormones, blood metabolites, and fatty acids, and arterial blood was sampled for blood pH and blood gas analysis. Milk was characterized for fatty acid composition. Blood pH was unchanged, but partial pressure of blood CO2, blood bicarbonate, base excess, and total CO2 declined with administration of bST. Serum triglycerides increased 89% in cows receiving bST. Blood urea nitrogen tended to decline in cows receiving bST. Serum cortisol, triiodothyronine, and thyroxine did not change, but insulin-like growth factor-1 increased 128% with bST use. Reduced milk short-chain fatty acids, increased milk long-chain fatty acids, and increased blood serum C18:1 fatty acid content occurred in cows administered bST and probably reflected tissue mobilization. Cows administered bST in hot weather had higher milk temperatures. Alterations in physiologic and metabolic measures in association with higher milk temperature suggest an interaction of bST use with hot, humid weather and reflect the need to minimize the effects of heat stress.     

The effect of dry period length and postpartum level of concentrate on milk production,energy balance,and plasma metabolites of dairy cows across the dry period and in early lactation

Shortening or omitting the dry period (DP) improves energy balance (EB) in early lactation because of a reduction in milk yield. Lower milk yield results in lower energy demands and requires less energy intake. The aim of this study was to evaluate the effects of DP length and concentrate level postpartum on milk yield, feed intake, EB, and plasma metabolites between wk ?4 and 7 relative to calving of cows of second parity or higher. Holstein-Friesian dairy cows (n = 123) were assigned randomly to 1 of 2 DP lengths: 0-d DP (n = 81) or 30-d DP (n = 42). Prepartum, cows with a 0-d DP received a lactation ration based on grass silage and corn silage (6.4 MJ of net energy for lactation/kg of dry matter). Cows with a 30-d DP received a dry cow ration based on grass silage, corn silage, and straw (5.4 MJ of net energy for lactation/kg of dry matter). Postpartum, all cows received the same basal lactation ration as provided to lactating cows prepartum. Cows with a 0-d DP were fed a low level of concentrate up to 6.7 kg/d based on the requirement for their expected milk yield (0-d DP-L; n = 40) or the standard level of concentrate up to 8.5 kg/d (0-d DP-S; n = 41), which was equal to the concentrate level for cows with a 30-d DP (30-d DP-S; n = 42) based on requirements for their expected milk yield. Prepartum dry matter intake, concentrate intake, basal ration intake, energy intake, plasma β-hydroxybutyrate (BHB), and insulin concentrations were greater and plasma free fatty acids (FFA) and glucose concentrations were lower, but EB was not different in cows with a 0-d DP compared with cows with a 30-d DP. During wk 1 to 3 postpartum, milk fat yield and plasma BHB concentration were lower and dry matter intake and concentrate intake were greater in cows with a 0-d DP compared with cows with a 30-d DP. During wk 4 to 7 postpartum, fat- and protein-corrected milk (FPCM), lactose content, and lactose and fat yield were lower in 0-d DP-L or 0-d DP-S cows compared with 30-d DP-S cows. Basal ration intake, EB, body weight, plasma glucose, and insulin and insulin-like growth factor-1 concentrations were greater and plasma FFA and BHB concentrations were lower in 0-d DP-L and 0-d DP-S cows compared with 30-d DP-S cows. Concentrate and energy intake were lower in 0-d DP-L cows than in 0-d DP-S or 30-d DP-S cows. Milk yield and concentrations of plasma metabolites did not differ in wk 4 to 7, although EB was lower in wk 6 and 7 postpartum in 0-d DP-L cows than in 0-d DP-S cows. In conclusion, a 0-d DP reduced milk yield and improved EB and metabolic status of cows in early lactation compared with a 30-d DP. Reducing the postpartum level of concentrate of cows with a 0-d DP did not affect fat- and protein-corrected milk yield or plasma FFA and BHB concentrations in early lactation but did reduce EB in wk 6 and 7 postpartum.     

Oxidative stress,NF-κB signaling,NLRP3 inflammasome,and caspase apoptotic pathways are activated in mammary gland of ketotic Holstein cows

Ketosis is a serious metabolic disorder characterized by systemic and hepatic oxidative stress, inflammation, and apoptosis, as well as reduced milk yield. Because of the paucity of data on mammary responses during ketosis, the aim of this study was to evaluate alterations in oxidative stress, NF-κB signaling, NLRP3 inflammasome, and caspase apoptotic pathways in mammary gland of dairy cows with ketosis. Blood, mammary gland tissue, and milk samples were collected from healthy cows [Control, blood concentration of β-hydroxybutyrate (BHB) <0.6 mM, n = 10] and cows with subclinical ketosis (SCK, blood concentration of BHB >1.2 mM and <3 mM, n = 10) or clinical ketosis (CK, blood concentration of BHB >3 mM, n = 10) at median 8 d in milk (range = 6–12). Compared with Control, serum concentration of glucose was lower (3.91 vs. 2.86 or 2.12 mM) in cows with SCK or CK, whereas concentrations of fatty acids (0.25 vs. 0.57 or 1.09 mM) and BHB (0.42 vs. 1.81 or 3.85 mM) were greater. Compared with Control, the percentage of milk fat was greater in cows with SCK or CK. In contrast, the percentage of milk protein was lower in cows with SCK or CK. We detected no differences in milk lactose content across groups. Compared with Control, activities of glutathione peroxidase, superoxide dismutase, and catalase were lower in mammary gland tissue of cows with SCK or CK. In contrast, concentrations of hydrogen peroxide and malondialdehyde were greater in cows with SCK or CK. Compared with Control, mRNA abundances of TNFA, IL6, and IL1B were greater in mammary tissues of cows with SCK or CK. In addition, activity of IKKβ and the ratio of phosphorylated inhibitor of κBα to IκBα, and of phosphorylated NF-κB p65 to NF-κB p65, were also greater in mammary tissues of cows with SCK or CK. Subclinical or clinical ketosis also led to greater activity of caspase 1 and protein abundance of caspase 1, NLRP3, Bax, caspase 3, and caspase 9. In contrast, abundance of the antiapoptotic protein was lower in SCK or CK cows. The data indicate that the mammary gland of SKC or CK cows undergoes severe oxidative stress, inflammation, and cell death.     

Effects of parity and body condition at parturition on endocrine and reproductive parameters of the cow

The effect of parity (multiparous vs primiparous) and body condition score (BCS; <3.0 or > or =3.0, lean vs fat) at parturition on metabolic and endocrine profiles from 1 month before to 2 months after parturition were studied in 42 Holstein cows grazing on improved pastures. BCS and milk production were determined every 2 weeks. Non-esterified fatty acids (NEFA), beta-hydroxy-butyrate (BHB), insulin, IGF-I, leptin, thyroxine (T4) and 3,3',5-tri-iodothyroinine (T3) were determined in plasma every 10 days. Progesterone was determined three times per week after parturition. Primiparous cows had a lower BCS during the early postpartum period and produced less milk than multiparous animals. Primiparous cows had higher NEFA concentrations and they presented more samples with BHB concentrations of >1 mmol/l than multiparous cows. Multiparous cows had higher T3, T4 and IGF-I concentrations, while fat cows had higher leptin and IGF-I concentrations. All hormone concentrations were diminished in the first week postpartum. Primiparous cows and fat cows presented a steeper decay of IGF-I and leptin around parturition than multiparous cows and lean cows. While thyroid hormones and IGF-I showed increasing concentrations from approximately day 30, leptin concentrations remained low until the end of the experimental period. The initiation of ovarian cyclicity was delayed in primiparous cows and especially in primiparous lean cows, consistent with longer intervals from parturition to first service and to conception. The endocrine signals most likely to inform the reproductive axis regarding a negative energy balance were IGF-I and leptin.