Combination of milk variables and on-farm data as an improved diagnostic tool for metabolic status evaluation in dairy cattle during the transition period.

Milk composition, particularly milk fatty acids, has been extensively studied as an indicator of the metabolic status of dairy cows during early lactation. In addition to milk biomarkers, on-farm sensor data also hold potential in providing insights into the metabolic health status of cows. While numerous studies have explored the collection of a wide range of sensor data from cows, the combination of milk biomarkers and on-farm sensor data remains relatively underexplored. Therefore, this study aims to identify associations between metabolic blood variables, milk variables, and various on-farm sensor data. Second, it seeks to examine the supplementary or substitutive potential of these data sources. Therefore, data from 85 lactations on metabolic status and on-farm data were collected during 3 wk before calving up to 5 wk after calving. Blood samples were taken on d 3, 6, 9, and 21 after calving for determination of ß-hydroxybutyrate (BHB), nonesterified fatty acids (NEFA), glucose, insulin-like growth factor-1 (IGF-1), insulin, and fructosamine. Milk samples were taken during the first 3 wk in lactation and analyzed by mid-infrared for fat, protein, lactose, urea, milk fatty acids, and BHB. Walking activity, feed intake, and body condition score (BCS) were monitored throughout the study. Linear mixed effect models were used to study the association between blood variables and (1) milk variables (i.e., milk models); (2) on-farm data (i.e., on-farm models) consisting of activity and dry matter intake analyzed during the dry period ([D]) and lactation ([L]) and BCS only analyzed during the dry period ([D]); and (3) the combination of both. In addition, to assess whether milk variables can clarify unexplained variation from the on-farm model and vice versa, Pearson marginal residuals from the milk and on-farm models were extracted and related to the on-farm and milk variables, respectively. The milk models had higher coefficient of determination (R2) than the on-farm models, except for IGF-1 and fructosamine. The highest marginal R2 values were found for BHB, glucose, and NEFA (0.508, 0.427, and 0.303 vs. 0.468, 0.358, and 0.225 for the milk models and on-farm models, respectively). Combining milk and on-farm data particularly increased R2 values of models assessing blood BHB, glucose, and NEFA concentrations with the fixed effects of the milk and on-farm variables mutually having marginal R2 values of 0.608, 0.566, and 0.327, respectively. Milk C18:1 was confirmed as an important milk variable in all models, but particularly for blood NEFA prediction. On-farm data were considerably more capable of describing the IGF-1 concentration than milk data (marginal R2 of 0.192 vs. 0.086), mainly due to dry matter intake before calving. The BCS [D] was the most important on-farm variable in relation to blood BHB and NEFA and could explain additional variation in blood BHB concentration compared with models solely based on milk variables. This study has shown that on-farm data combined with milk data can provide additional information concerning the metabolic health status of dairy cows. On-farm data are of interest to be further studied in predictive modeling, particularly because early warning predictions using milk data are highly challenging or even missing.

Milk yield residuals and their link with the metabolic status of dairy cows in the transition period.

The transition period is one of the most challenging periods in the lactation cycle of high-yielding dairy cows. It is commonly known to be associated with diminished animal welfare and economic performance of dairy farms. The development of data-driven health monitoring tools based on on-farm available milk yield development has shown potential in identifying health-perturbing events. As proof of principle, we explored the association of these milk yield residuals with the metabolic status of cows during the transition period. Over 2 yr, 117 transition periods from 99 multiparous Holstein-Friesian cows were monitored intensively. Pre- and postpartum dry matter intake was measured and blood samples were taken at regular intervals to determine ß-hydroxybutyrate, nonesterified fatty acids (NEFA), insulin, glucose, fructosamine, and IGF1 concentrations. The expected milk yield in the current transition period was predicted with 2 previously developed models (nextMILK and SLMYP) using low-frequency test-day (TD) data and high-frequency milk meter (MM) data from the animal's previous lactation, respectively. The expected milk yield was subtracted from the actual production to calculate the milk yield residuals in the transition period (MRT) for both TD and MM data, yielding MRTTD and MRTMM. When the MRT is negative, the realized milk yield is lower than the predicted milk yield, in contrast, when positive, the realized milk yield exceeded the predicted milk yield. First, blood plasma analytes, dry matter intake, and MRT were compared between clinically diseased and nonclinically diseased transitions. MRTTD and MRTMM, postpartum dry matter intake and IGF1 were significantly lower for clinically diseased versus nonclinically diseased transitions, whereas ß-hydroxybutyrate and NEFA concentrations were significantly higher. Next, linear models were used to link the MRTTD and MRTMM of the nonclinically diseased cows with the dry matter intake measurements and blood plasma analytes. After variable selection, a final model was constructed for MRTTD and MRTMM, resulting in an adjusted R2 of 0.47 and 0.73, respectively. While both final models were not identical the retained variables were similar and yielded comparable importance and direction. In summary, the most informative variables in these linear models were the dry matter intake postpartum and the lactation number. Moreover, in both models, lower and thus also more negative MRT were linked with lower dry matter intake and increasing lactation number. In the case of an increasing dry matter intake, MRTTD was positively associated with NEFA concentrations. Furthermore, IGF1, glucose, and insulin explained a significant part of the MRT. Results of the present study suggest that milk yield residuals at the start of a new lactation are indicative of the health and metabolic status of transitioning dairy cows in support of the development of a health monitoring tool. Future field studies including a higher number of cows from multiple herds are needed to validate these findings.

Transition cow clusters with distinctive antioxidant ability and their relation to performance and metabolic status in early lactation.

Metabolic and oxidative stress have been characterized as risk factors during the transition period from pregnancy to lactation. Although mutual relations between both types of stress have been suggested, they rarely have been studied concomitantly. For this, a total of 99 individual transition dairy cows (117 cases, 18 cows sampled during 2 consecutive lactations) were included in this experiment. Blood samples were taken at -7, 3, 6, 9, and 21 d relative to calving and concentrations of metabolic parameters (glucose, ß-hydroxybutyric acid (BHBA), nonesterified fatty acids, insulin, insulin-like growth factor 1, and fructosamine) were determined. In the blood samples of d 21, biochemical profiles related to liver function and parameters related to oxidative status were determined. First, cases were allocated to 2 different BHBA groups (ketotic vs. nonketotic, N:n = 20:33) consisting of animals with an average postpartum BHBA concentration and at least 2 out of 4 postpartum sampling points exceeding 1.2 mmol/L or remaining below 0.8 mmol/L, respectively. Second, oxidative parameters [proportion of oxidized glutathione to total glutathione in red blood cells (%)], activity of glutathione peroxidase, and of superoxide dismutase, concentrations of malondialdehyde and oxygen radical absorbance capacity were used to perform a fuzzy C-means clustering. From this, 2 groups were obtained [i.e., lower antioxidant ability (LAA80%, n = 31) and higher antioxidant ability (HAA80%, n = 19)], with 80% referring to the cutoff value for cluster membership. Increased concentrations of malondialdehyde, decreased superoxide dismutase activity, and impaired oxygen radical absorbance capacity were observed in the ketotic group compared with the nonketotic group, and inversely, the LAA80% group showed increased concentrations of BHBA. In addition, the concentration of aspartate transaminase was higher in the LAA80% group compared with the HAA80% group. Both the ketotic and LAA80% groups showed lower dry matter intake. However, a lower milk yield was observed in the LAA80% group but not in the ketotic group. Only 1 out of 19 (5.3%) and 3 out of 31 (9.7%) cases from the HAA80% and LAA80% clusters belong to the ketotic and nonketotic group, respectively. These findings suggested that dairy cows vary in oxidative status at the beginning of the lactation, and fuzzy C-means clustering allows to classify observations with distinctive oxidative status. Dairy cows with higher antioxidant capacity in early lactation rarely develop ketosis.

Prediction of metabolic status of dairy cows in early lactation using milk fatty acids and test-day variables.

Early lactation metabolic imbalance is an important physiological change affecting the health, production, and reproduction of dairy cows. The aims of this study were (1) to evaluate the potential of test-day (TD) variables with or without milk fatty acids (FA) content to classify metabolically imbalanced cows and (2) to evaluate the robustness of the metabolic classification with external data. A data set was compiled from 3 experiments containing plasma ß-hydroxybutyrate, nonesterified FA, glucose, insulin-like growth factor-I, FA proportions in milk fat, and TD variables collected from 244 lactations in wk 2 after calving. Based on the plasma metabolites, 3 metabolic clusters were identified using fuzzy c-means clustering and the probabilistic membership value of each cow to the 3 clusters was determined. Comparing the mean concentration of the plasma metabolites, the clusters were differentiated into metabolically imbalanced, moderately impacted, and balanced. Following this, the 2 metabolic status groups identified were imbalanced cows (n = 42), which were separated from what we refer to as "others" (n = 202) based on the membership value of each cow for the imbalanced cluster using a threshold of 0.5. The following 2 FA data sets were composed: (1) FA (groups) having high prediction accuracy by Fourier-transform infrared spectroscopy and, thus, have practical significance, and (2) FA (groups) formerly identified as associated with metabolic changes in early lactation. Metabolic status prediction models were built using FA alone or combined with TD variables as predictors of metabolic groups. Comparison was made among models and external evaluations were performed using an independent data set of 115 lactations. The area under the receiver operating characteristics curve of the models was between 75 and 91%, indicating their moderate to high accuracy as a diagnostic test for metabolic imbalance. The addition of FA groups to the TD models enhanced the accuracy of the models. Models with FA and TD variables showed high sensitivities (80-88%). Specificities of these models (73-79%) were also moderate and acceptable. The accuracy of the FA models on the external data set was high (area under the receiver operating characteristics curve between 76 and 84). The persistently good performance of models with Fourier-transform infrared spectroscopy-quantifiable FA on the external data set showed their robustness and potential for routine screening of metabolically imbalanced cows in early lactation.

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 ß-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 (AUCROC) = 0.69]. The combination of the features of the DHI model with BHB [MIR] and MFA [MIR] powerfully increased the predictive performance (AUCROC = 0.81). The model based on the detailed MFA profile determined by GC analysis did not outperform (AUCROC = 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 (AUCROC DHI + BHB [MIR] + MFA [MIR] model at d 3: 0.75; AUCROC 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.

Different reticuloruminal pH metrics of high-yielding dairy cattle during the transition period in relation to metabolic health, activity, and feed intake.

The measurement of pH in the reticulorumen in combination with a time-pH threshold has been widely applied in research to diagnose subacute ruminal acidosis. However, other pH metrics also have biological value. In this study, 44 animals were monitored during the transition period using reticuloruminal pH boluses. Traditional and more complex pH characteristics were calculated to characterize the reticuloruminal pH profile: time pH <6, slope of a logistic cumulative pH curve (ß0), and deviations [squared error (SqEr)] from pH predictions based on a harmonic static model. In this study, we aimed to examine the associations between those pH metrics and metabolic health parameters, feed intake, and activity. Finally, to describe the reticuloruminal pH dynamically, we also constructed a dynamic linear model. The results of this model were studied in relation to feed intake. All pH parameters were mutually correlated (particularly ß0 and SqEr; mean Pearson correlation of -0.52). pH patterns, rather than time pH <6, were associated with metabolic health and feed intake: high variation in daily pH (ß0 parameter) was reflected in higher blood concentrations of nonesterified fatty acids. Moreover, pH deviations of the harmonic model were negatively associated with feed intake and rumination behavior. This research confirms the biological importance of pH metrics focusing on pH variation and pH deviations and provides deeper insight into its associations with metabolic health status, feed intake, and activity during early lactation.

Subacute ruminal acidosis phenotypes in periparturient dairy cows differ in ruminal and salivary bacteria and in the in vitro fermentative activity of their ruminal microbiota.

Both ruminal microbial structure and functionality might play a role in inter-individual variation in susceptibility for subacute rumen acidosis (SARA) observed in dairy cows. The aims of this study were to determine whether differences between cows with distinct SARA susceptibility were reflected in distinct (1) ruminal microbial communities, (2) salivary bacterial communities, and (3) fermentative capacity of ruminal microbiota assessed in vitro. To test this hypothesis, rumen samples were collected via an esophageal tube on 21 d postpartum from 38 multiparous Holstein cows, which were classified into 4 groups differing in median and mean time of reticular pH below 6 as well as area under the curve of pH below 6.0. During the 21 d postpartum, all cows within a group fulfilled following criteria: susceptible (S, n = 10; mean or median ≥180 min/d), moderately susceptible (MS, n = 7; 60 min/d < mean time of pH below 6 < 180 min/d, and median time of pH below 6 <180 min/d), moderately unsusceptible (MU, n = 11; 10 min/d < mean < 60 min/d, and median time of pH below 6 ≤30 min/d), or unsusceptible (U, n = 10; median = 0 min/d, and mean <10 min/d). Groups did not differ in total daily dry matter intake nor in total, roughage, or concentrate intake during daily 6-h time intervals. Rumen bacterial α-diversity did not differ among groups, but ß-diversity varied and bacterial 16S rRNA gene copy numbers were lower in S compared with U cows. The relative abundance of genera Streptococcus, Sharpea, Prevotellaceae_YAB2003, Succinivibrionaceae_UCG-001, Ruminococcus, and Ruminococcaceae_UCG-001 were higher in S compared with U cows. In contrast, Lachnospiraceae_ND3007 and Oscillospiraceae_V9D2013 were more abundant in U cows. Although pH-associated, inter-animal differences were also observed in the salivary bacteria, common differences in ruminal and salivary bacterial genera were limited. The functionality of the rumen microbiota was evaluated in vitro through exposure of the microbial inoculum of S and U cows to an anaerobic buffer at pH 5.8 and 6.8, in the presence of sterile supernatant of their own and of dry cows' rumen fluid (2 × 2 design). Generally, the S inoculum produced more volatile fatty acids, except at low pH with dry cows' supernatant, where volatile fatty acid production was completely impaired and lactate accumulation was highest. Compared with the microbes of U cows, microbes of S cows showed less fermentative activity in situations with 2 stress factors (low pH and an unfamiliar environment, i.e., rumen fluid supernatant of dry cows).