Effects of feeding rumen-protected lysine during the postpartum period on performance and amino acid profile in dairy cows: A meta-analysis.

Lysine is one of the limiting AA in the diets of dairy cows and is typically fed as rumen-protected Lys (RPL). We hypothesized that supplementation of RPL during the postpartum period would improve the productive performance in dairy cows. Objectives were to use meta-analytic methods to explore the effects of feeding RPL on performance and blood AA profile in lactating dairy cows. An additional objective was to identify an optimal concentration (%) of Lys in MP (LYSMP) and determine if responses to LYSMP were associated with the concentration (%) of Met in MP (METMP). The literature was systematically reviewed, and 13 experiments, comprising 40 treatment means and 594 lactating cows, were included in the meta-analysis. All experiments had a nonsupplemental control (CON; n = 17 treatment means), or a group supplemented with RPL (n = 23 treatment means). Cows supplemented with RPL were supplied additionally with a mean (±standard deviation) 19.3 ± 10.3 g/d metabolizable Lys (5.1-40.6 g/d). Meta-analytical statistics were used to estimate the weighted mean difference in STATA. Mixed models were fitted to the data to investigate the linear and quadratic effects of LYSMP, METMP, and interactions between LYSMP and METMP. All models included the random effect of experiment and weighting by the inverse of the SE of the means squared. Cows that began receiving RPL in early lactation (≤90 DIM) or for an extended duration (≥70 DIM) produced 1.51 kg/d more milk compared with CON cows. Increasing digestible LYSMP from 6.5% to 8.5% linearly increased yields of milk, FCM, ECM, and milk fat by 1.8, 2.5, 2.4, and 0.10 kg/d, respectively, and tended to increase milk protein yield and body weight gain by 0.07 and 0.09 kg/d, respectively, without a concurrent increase in DMI. Interactions between the linear effects of LYSMP and METMP were observed for FCM/DMI or ECM/DMI. In a diet with low METMP (e.g., 1.82% of MP), a digestible supply of 7.40% LYSMP would result in 1.46 and 1.47 kg/kg FCM/DMI or ECM/DMI, respectively; however, with high digestible METMP (e.g., 2.91% of MP), supplying 7.40% of digestible LYSMP would result in 1.68 and 1.62 kg/kg FCM/DMI or ECM/DMI, respectively. Increasing digestible LYSMP from 6.5% to 8.5% linearly increased blood concentrations of Lys by 16.6 µM, whereas blood concentrations of Met and Ala decreased by 4.6 and 6.0 µM, respectively. Nevertheless, an interaction was also observed between LYSMP and METMP for blood concentrations of total EAA because as METMP increased, the positive response to LYSMP on total EAA was also increased, suggesting a competitive mobilization of AA and their utilization in various body tissues. Only 4 out of the 13 experiments in this meta-analysis involved primiparous cows; thus, insufficient data were available to understand the role of supplemental RPL in primiparous cows. Collectively, feeding RPL improved productive performance, and the increments were maximized up to 9.25% of LYSMP in multiparous dairy cows.

Assessing different cross-validation schemes for predicting novel traits using sensor data: an application to dry matter intake and residual feed intake using milk spectral data.

Feed efficiency is important for economic profitability of dairy farms; however, recording daily dry matter intakes (DMI) is expensive. Our objective was to investigate the potential use of milk mid-infrared (MIR) spectral data to predict proxy phenotypes for DMI based on different cross-validation schemes. We were specifically interested in comparisons between a model that included only MIR data (Model M1), a model that incorporated different energy sink predictors, such as body weight, body weight change, and milk energy (Model M2), and an extended model that incorporated both energy sinks and MIR data (Model M3). Models M2 and M3 also included various cow level variables (stage of lactation, age at calving, parity) such that any improvement in model performance from M2 to M3, whether through a smaller root mean squared error (RMSE) or a greater squared predictive correlation (R2), could indicate a potential benefit of MIR to predict residual feed intake. The data used in our study originated from a multi-institutional project on the genetics of feed efficiency in US Holsteins. Analyses were conducted on 2 different trait definitions based on different period lengths: averaged across weeks vs. averaged across 28-d. Specifically, there were 19,942 weekly records on 1,812 cows across 46 experiments or cohorts and 3,724 28-d records on 1,700 cows across 43 different cohorts. The cross-validation analyses involved 3 different k-fold schemes. First, a 10-fold cow-independent cross-validation was conducted whereby all records from any one cow were kept together in either training or test sets. Similarly, a 10-fold experiment-independent cross-validation kept entire experiments together whereas a 4-fold herd-independent cross-validation kept entire herds together in either training or test sets. Based on cow-independent cross-validation for both weekly and 28-d DMI, adding MIR predictors to energy sinks (Models M3 vs M2) significantly (P < 10-10) reduced average RMSE to 1.59 kg and increased average R2 to 0.89. However, adding MIR to energy sinks (M3) to predict DMI either within an experiment-independent or herd-independent cross-validation scheme seemed to demonstrate no merit (P > 0.05) compared with an energy sink model (M2) for either R2 or RMSE (respectively, 0.68 and 2.55 kg for M2 in herd-independent scheme). We further noted that with broader cross-validation schemes, i.e., from cow-independent to experiment-independent to herd-independent schemes, the mean and slope bias increased. Given that proxy DMI phenotypes for cows would need to be almost entirely generated in herds having no DMI or training data of their own, herd-independent cross-validation assessments of predictive performance should be emphasized. Hence, more research on predictive algorithms suitable for broader cross-validation schemes and a more earnest effort on calibration of spectrophotometers against each other should be considered.

Increasing the prepartum dose of rumen-protected choline: Effects of maternal choline supplementation on growth, feed efficiency, and metabolism in Holstein and Holstein × Angus calves.

Feeding pregnant cows rumen-protected choline (RPC) may have the potential to affect the growth and health of offspring, but little is known about the optimal dose, or the potential mechanisms of action. The objectives of this experiment were to 1) determine if increasing RPC supplementation during late gestation in multiparous Holstein cows would improve calf growth and 2) determine if maternal choline supplementation alters global DNA methylation patterns. Pregnant multiparous Holstein cows (n = 116) were randomly assigned to diets targeting 0g choline ion (0.0 ± 0.000 choline ion, %DM, control; CTL), 15g of choline ion (recommended dose; RD) from an established RPC product (0.10 ± 0.004 choline ion, %DM, RPC1RD; ReaShure, Balchem Corp.; positive control), or 15g (0.09 ± 0.004 choline ion, %DM, RPC2RD) or 22g (0.13 ± 0.005 choline ion, %DM, high dose; RPC2HD) of choline ion from a concentrated RPC prototype (RPC2; Balchem Corp.). Treatments were mixed into a total mixed ration and cows had ad libitum access via a roughage intake control system (Hokofarm Group, Marknesse, Netherlands). All female Holstein (n = 49) and Holstein × Angus calves (male, n = 18; female, n = 30) were enrolled and fed colostrum from a cow within the same treatment. Holstein calves and Holstein × Angus calves were fed an accelerated and traditional milk replacer program, respectively, and offered ad libitum access to calf starter. Jugular vein blood samples were collected, and body weight was measured at 7, 14, 28, 42, and 56 d of age. Categorical treatment and continuous effects of actual prepartum maternal choline ion intake were analyzed using mixed effect models. An interaction of treatment with sex, nested within breed, resulted in any choline treatment increasing the proportion of methylated whole blood DNA in male, but not female calves. Although 37% of Holstein calves across all treatments experienced abomasal bloat, no evidence for differences in health measurements (signs of respiratory disease and fecal consistency) were observed across treatments. During the first 2 wk of life in Holstein calves, RPC2HD tended to increase average daily gain (ADG) and feed efficiency (FE) compared with CTL and increasing maternal choline ion intake linearly increased ADG and FE. Maternal choline supplementation increased plasma glucose compared with CTL, while increasing serum insulin-like growth factor-1 and decreasing serum lipopolysaccharide binding protein at 7 d of age in Holstein calves. In Holstein × Angus calves, the effect of treatment on ADG tended to interact with sex: in males, RPC2HD increased ADG after 2 wk of life compared with CTL, without evidence of a treatment effect in female calves. Increasing maternal choline ion intake linearly increased ADG after 2 wk of age in male Holstein × Angus calves, while quadratically increasing FE in both sexes. Altered global DNA methylation patterns in male Holstein × Angus calves, and changes in blood metabolites in Holstein calves, provide 2 potential mechanisms for observed improvements in calf growth. Continuous treatment models demonstrated that the effects of maternal choline supplementation are sensitive to the amount of maternal choline ion intake, with greater benefit to calves observed at higher maternal intakes.

Increasing the prepartum dose of rumen-protected choline: Effects on milk production and metabolism in high-producing Holstein dairy cows.

Peripartum rumen-protected choline (RPC) supplementation is beneficial for cow health and production, yet the optimal dose is unknown. In vivo and in vitro supplementation of choline modulates hepatic lipid, glucose, and methyl donor metabolism. The objective of this experiment was to determine the effects of increasing the dose of prepartum RPC supplementation on milk production and blood biomarkers. Pregnant multiparous Holstein cows (n = 116) were randomly assigned to one of 4 prepartum choline treatments that were fed from -21 d relative to calving (DRTC) until calving. From calving until +21 DRTC, cows were fed diets targeting 0 g/d choline ion (control, CTL) or the recommended dose (15 g/d choline ion; RD) of the same RPC product that they were fed prepartum. The resulting treatments targeted: (1) 0 g/d pre- and postpartum [0.0 ± 0.000 choline ion, percent of dry matter (%DM); CTL]; (2) 15 g/d pre- and postpartum of choline ion from an established product (prepartum: 0.10 ± 0.004 choline ion, %DM; postpartum: 0.05 ± 0.004 choline ion, %DM; ReaShure, Balchem Corp.; RPC1RD▸RD); (3) 15 g/d pre- and postpartum of choline ion from a concentrated RPC prototype (prepartum: 0.09 ± 0.004 choline ion, %DM; postpartum: 0.05 ± 0.003 choline ion, %DM; RPC2, Balchem Corp.; RPC2RD▸RD); or (4) 22 g/d prepartum and 15 g/d postpartum from RPC2 [prepartum: 0.13 ± 0.005 choline ion, %DM; postpartum: 0.05 ± 0.003 choline ion, %DM; high prepartum dose (HD), RPC2HD▸RD]. Treatments were mixed into a total mixed ration, and cows had ad libitum access via a roughage intake control system (Hokofarm Group). From calving to +21 DRTC, all cows were fed a common base diet and treatments were mixed into the total mixed ration (supplementation period, SP). Thereafter, all cows were fed a common diet (0 g/d choline ion) until +100 DRTC (postsupplementation period, postSP). Milk yield was recorded daily and composition analyzed weekly. Blood samples were obtained via tail vessel upon enrollment, approximately every other day from -7 to +21 DRTC, and at +56 and +100 DRTC. Feeding any RPC treatment reduced prepartum dry matter intake compared with CTL. During the SP, no evidence for a treatment effect on energy-corrected milk (ECM) yield was found, but during the postSP, RPC1RD▸RD and RPC2RD▸RD treatments tended to increase ECM, protein, and fat yields. During the postSP, the RPC1RD▸RD and RPC2RD▸RD treatments tended to increase, and RPC2HD▸RD increased, the de novo proportion of total milk fatty acids. During the early lactation SP, RPC2HD▸RD tended to increase plasma fatty acids and ß-hydroxybutyrate concentrations, and RPC1RD▸RD and RPC2RD▸RD reduced blood urea nitrogen concentrations compared with CTL. The RPC2HD▸RD treatment reduced early lactation serum lipopolysaccharide binding protein compared with CTL. Overall, peripartum RPC supplementation at the recommended dose tended to increase ECM yield postSP, but no evidence was seen of an additional benefit on milk production with an increased prepartum dose of choline ion. The effects of RPC on metabolic and inflammatory biomarkers support the potential for RPC supplementation to affect transition cow metabolism and health and may support the production gains observed.

Novel genetic parameters for genetic residual feed intake in dairy cattle using time series data from multiple parities and countries in North America and Europe.

Residual feed intake is viewed as an important trait in breeding programs that could be used to enhance genetic progress in feed efficiency. In particular, improving feed efficiency could improve both economic and environmental sustainability in the dairy cattle industry. However, data remain sparse, limiting the development of reliable genomic evaluations across lactation and parity for residual feed intake. Here, we estimated novel genetic parameters for genetic residual feed intake (gRFI) across the first, second, and third parity, using a random regression model. Research data on the measured feed intake, milk production, and body weight of 7,379 cows (271,080 records) from 6 countries in 2 continents were shared through the Horizon 2020 project Genomic Management Tools to Optimise Resilience and Efficiency, and the Resilient Dairy Genome Project. The countries included Canada (1,053 cows with 47,130 weekly records), Denmark (1,045 cows with 72,760 weekly records), France (329 cows with 16,888 weekly records), Germany (938 cows with 32,614 weekly records), the Netherlands (2,051 cows with 57,830 weekly records), and United States (1,963 cows with 43,858 weekly records). Each trait had variance components estimated from first to third parity, using a random regression model across countries. Genetic residual feed intake was found to be heritable in all 3 parities, with first parity being predominant (range: 22-34%). Genetic residual feed intake was highly correlated across parities for mid- to late lactation; however, genetic correlation across parities was lower during early lactation, especially when comparing first and third parity. We estimated a genetic correlation of 0.77 ± 0.37 between North America and Europe for dry matter intake at first parity. Published literature on genetic correlations between high input countries/continents for dry matter intake support a high genetic correlation for dry matter intake. In conclusion, our results demonstrate the feasibility of estimating variance components for gRFI across parities, and the value of sharing data on scarce phenotypes across countries. These results can potentially be implemented in genetic evaluations for gRFI in dairy cattle.

Feeding behavior parameters and temporal patterns in mid-lactation Holstein cows across a range of residual feed intake values.

Residual feed intake (RFI) is a measurement of the difference between actual and predicted feed intake when adjusted for energy sinks; more efficient cows eat less than predicted (low RFI) and inefficient cows eat more than predicted (high RFI). Data evaluating the relationship between RFI and feeding behaviors (FB) are limited in dairy cattle; therefore, the objective of this study was to determine daily and temporal FB in mid-lactation Holstein cows across a range of RFI values. Mid-lactation Holstein cows (n = 592 multiparous; 304 primiparous) were enrolled in 17 cohorts at 97 ± 26 d in milk (± standard deviation), and all cows within a cohort were fed a common diet using automated feeding bins. Cow RFI was calculated as the difference between predicted and observed dry matter intake (DMI) after accounting for parity, days in milk, milk energy, metabolic body weight and change, and experiment. The associations between RFI and FB at the level of meals and daily totals were evaluated using mixed models with the fixed effect of RFI and the random effects of cow and cohort. Daily temporal FB analyses were conducted using 2-h blocks and analyzed using mixed models with the fixed effects of RFI, time, RFI × time, and cohort, and the random effect of cow (cohort). There was a positive linear association between RFI and DMI in multiparous cows and a positive quadratic relationship in primiparous cows, where the rate of increase in DMI was less at higher RFI. Eating rate, DMI per meal, and size of the largest daily meal were positively associated with RFI. Daily temporal analysis of FB revealed an interaction between RFI and time for eating rate in multiparous and primiparous cows. The eating rate increased with greater RFI at 11 of 12 time points throughout the day, and eating rate differed across RFI between multiple time points. There tended to be an interaction between RFI and time for eating time and bin visits in multiparous cows but not primiparous cows. Overall, there was a time effect for all FB variables, where DMI, eating time and rate, and bin visits were greatest after the initial daily feeding at 1200 h, increased slightly after each milking, and reached a nadir at 0600 h (6 h before feeding). Considering the relationship between RFI and eating rate, additional efforts to determine cost-effective methods of quantifying eating rate in group-housed dairy cows is warranted. Further investigation is also warranted to determine if management strategies to alter FB, especially eating rate, can be effective in increasing feed efficiency in lactating dairy cattle.

Predicting dry matter intake in mid-lactation Holstein cows using point-in-time data streams available on dairy farms.

Quantifying dry matter intake (DMI) in lactating dairy cows is important for determining feed efficiency; however, there are no methods for economically quantifying individual cow DMI on dairy farms where cows are group-fed. Attempts have been made to model DMI using cow factors, milk production, milk infrared spectra, and behavioral sensors with reasonable success. Other data streams are available on the farm that may contribute to DMI predictions. In this study, our objective was to model DMI with multiple linear regression using data from a single point-in-time that can easily be accessed on-farm. Candidate predictor variables included cow descriptors, milk yield and composition, milk fatty acid profile, and production and efficiency predicting transmitting abilities (PTA). Observations of DMI were obtained from 350 cows across 6 cohorts using individual feed bunks. The cow to bunk ratio was 2:1, with an overall bunk occupation rate of 32% throughout the day. The following models were developed sequentially with milk data obtained from a single morning milking and other data from the same day: model B (production, metabolic body weight, body condition score, lactation category, and week of lactation), model BC [model B + fatty acid (FA) content], model BY (model B + FA yield), model BPE (model B + production and efficiency PTA), model BYP (model BY + production PTA), model BYE (model BY + efficiency PTA), and model BYPE (model BY + production and efficiency PTA). Outcome variables predicted in these models were the DMI on the previous day or current day relative to the morning milk sample. The predictions for DMI on the previous day outperformed current day DMI in every model for which they were both determined. Addition of milk FA and PTA as candidate predictor variable types to the models resulted in enhanced predictive ability, with incremental enhancements when combined. The most robust model (BYPE) included cow descriptors, protein and FA yields, and PTA for milk and residual feed intake. Model BYPE described 21 to 32% more of the variation in DMI (based on concordance correlation coefficient) than when other common DMI models were applied to the same data set. Overall, reasonable performance of models including single point-in-time cow descriptors, milk and FA production, and production and efficiency PTA commonly available to dairy farmers through dairy herd improvement programs offer an opportunity for on-farm prediction of DMI, yet further improvement may be possible.

Multiple-trait random regression modeling of feed efficiency in US Holsteins.

Residual feed intake (RFI) and feed saved (FS) are important feed efficiency traits that have been increasingly considered in genetic improvement programs. Future sustainability of these genetic evaluations will depend upon greater flexibility to accommodate sparsely recorded dry matter intake (DMI) records on many more cows, especially from commercial environments. Recent multiple-trait random regression (MTRR) modeling developments have facilitated days in milk (DIM)-specific inferences on RFI and FS, particularly in modeling the effect of change in metabolic body weight (MBW). The MTRR analyses, using daily data on the core traits of DMI, MBW, and milk energy (MilkE), were conducted separately for 2,532 primiparous and 2,379 multiparous US Holstein cows from 50 to 200 DIM. Estimated MTRR variance components were used to derive genetic RFI and FS and DIM-specific genetic partial regressions of DMI on MBW, MilkE, and change in MBW. Estimated daily heritabilities of RFI and FS varied across lactation for both primiparous (0.05-0.07 and 0.11-0.17, respectively) and multiparous (0.03-0.13 and 0.10-0.17, respectively) cows. Genetic correlations of RFI across DIM varied (>0.05) widely compared with FS (>0.54) within either parity class. Heritability estimates based on average lactation-wise measures were substantially larger than daily heritabilities, ranging from 0.17 to 0.25 for RFI and from 0.35 to 0.41 for FS. The partial genetic regression coefficients of DMI on MBW (0.11 to 0.16 kg/kg0.75 for primiparous and 0.12 to 0.14 kg/kg0.75 for multiparous cows) and of DMI on MilkE (0.45 to 0.68 kg/Mcal for primiparous and 0.36 to 0.61 kg/Mcal for multiparous cows) also varied across lactation. In spite of the computational challenges encountered with MTRR, the model potentially facilitates an efficient strategy for harnessing more data involving a wide variety of data recording scenarios for genetic evaluations on feed efficiency.

Effects of rumen-protected choline supplementation in Holstein dairy cows during electric heat blanket-induced heat stress.

Dairy cows experiencing heat stress (HS) attempt to thermoregulate through multiple mechanisms, such as reducing feed intake and milk production and altering blood flow to increase heat dissipation. Effects of choline on energy metabolism and immune function may yield it a viable nutritional intervention to mitigate negative effects of HS. The primary objective of this experiment was to determine if supplementation of rumen-protected choline during, or before and during, an increased heat load would ameliorate the negative effects of HS on production and immune status. Heat stress was induced via an electric heat blanket model with a 3-d baseline period and 7-d HS period for all cows. Multiparous mid-lactation (208 ± 31 days in milk) Holstein cows were fed the same basal herd diet, blocked by pre-experiment milk yield, and randomly assigned to receive one of the following: (1) no rumen-protected (RP) choline (n = 7); (2) RP choline (60 g/d) via top-dress during the HS period (n = 8); or (3) RP choline (60 g/d) via top-dress during the baseline and HS periods (n = 8). Imposing HS via electric heat blanket raised respiration rate with all cows surpassing the HS threshold of 60 breaths/min. The increase in respiration rate tended to be ameliorated with either schedule of RP choline supplementation. Milk yield tended to increase when RP choline was supplemented in both the baseline period and during HS. Supplementation of RP choline tended to reduce blood fatty acid and triglyceride and tended to increase the revised quantitative insulin sensitivity check index. The role of RP choline supplementation to partially ameliorate the effects of HS should be further explored as a potential nutritional strategy to mitigate the negative consequences of HS on health and production.

Comparison of methods to predict feed intake and residual feed intake using behavioral and metabolite data in addition to classical performance variables.

Predicting dry matter intake (DMI) and feed efficiency by leveraging the use of data streams available on farm could aid efforts to improve the feed efficiency of dairy cattle. Residual feed intake (RFI) is the difference between predicted and observed feed intake after accounting for body size, body weight change, and milk production, making it a valuable metric for feed efficiency research. Our objective was to develop and evaluate DMI and RFI prediction models using multiple linear regression (MLR), partial least squares regression, artificial neural networks, and stacked ensembles using different combinations of cow descriptive, performance, sensor-derived behavioral (SMARTBOW; Zoetis), and blood metabolite data. Data were collected from mid-lactation Holstein cows (n = 124; 102 multiparous, 22 primiparous) split equally between 2 replicates of 45-d duration with ad libitum access to feed. Within each predictive approach, 4 data streams were added in sequence: dataset M (week of lactation, parity, milk yield, and milk components), dataset MB (dataset M plus body condition score and metabolic body weight), dataset MBS (dataset MB plus sensor-derived behavioral variables), and dataset MBSP (dataset MBS plus physiological blood metabolites). The combination of 4 datasets and 4 analytical approaches resulted in 16 analyses of DMI and RFI, using variables averaged within cow across the study period. Additional models using weekly averaged data within cow and study were built using all predictive approaches for datasets M, MB, and MBS. Model performance was assessed using the coefficient of determination, concordance correlation coefficient, and root mean square error of prediction. Predictive models of DMI performed similarly across all approaches, and models using dataset MBS had the greatest model performance. The best approach-dataset combination was MLR-dataset MBS, although several models performed similarly. Weekly DMI models had the greatest performance with MLR and partial least squares regression approaches. Dataset MBS models had incrementally better performance than datasets MB and M. Within each approach-dataset combination, models with DMI averaged over the study period had slightly greater model performance than DMI averaged weekly. Predictive performance of all RFI models was poor, but slight improvements when using MLR applied to dataset MBS suggest that rumination and activity behaviors may explain some of the variation in RFI. Overall, similar performance of MLR, compared with machine learning techniques, indicates MLR may be sufficient to predict DMI. The improvement in model performance with each additional data stream supports the idea of integrating data streams to improve model predictions and farm management decisions.

ADSA Foundation Scholar Award: Influencing hepatic metabolism: Can nutrient partitioning be modulated to optimize metabolic health in the transition dairy cow?

Hepatic de novo production of glucose and oxidation of fatty acids are critical in supporting milk production during the transition to lactation period. During this period of metabolic challenge, there is an increase in fatty acids taken up by the liver. Although the primary fate for these fatty acids is complete oxidation, alternative fates include incomplete oxidation via ketogenesis, storage within the liver as triglycerides (TG), and secretion of TG within very low density lipoproteins. Influencing the relative capacity of these pathways, and thus shifting nutrient partitioning, may allow for improved hepatic efficiency and metabolic health. Hepatic nutrient partitioning reflects complex regulation of key metabolic pathways by factors such as fatty acids and other substrates. Relative flux of fatty acid through oxidation or re-esterification to TG leads to the onset of metabolic disorders that are associated with negative production outcomes, such as hyperketonemia and fatty liver. Although recent work has focused on understanding how stored TG are lipolyzed for subsequent oxidation, the mechanism and regulation of this remains unclear. The source of mobilized fatty acids is similarly important, both in terms of amount and profile of fatty acids mobilized. There is likely a complex, coordinated whole-body response, given that fatty acids mobilized from adipose tissue affect hepatic regulation. Fatty acids mobilized from adipose tissue have regulatory effects on genes such as pyruvate carboxylase; however, in vivo work suggests there may also be other influences resulting in differential regulation between cows that subsequently develop sub-clinical ketosis and those that do not. Optimizing nutrient partitioning between critical metabolic pathways may allow for nutritional opportunities to reduce incidence of metabolic challenges and improve feed efficiency. Although further research is needed to continue refining our understanding of the intricate balance regulating hepatic metabolism, shifting nutrient partitioning may be key in supporting both efficiency and metabolic health.

Symposium review: Big data, big predictions: Utilizing milk Fourier-transform infrared and genomics to improve hyperketonemia management.

Negative animal health and performance outcomes are associated with disease incidences that can be labor-intensive, costly, and cumbersome for many farms. Amelioration of unfavorable outcomes through early detection and treatment of disease has emphasized the value of improving health monitoring. Although the value is recognized, detecting hyperketonemia (HYK) is still difficult for many farms to do practically and efficiently. Increasing data streams available to farms presents opportunities to use data to better monitor cow and herd health; however, challenges remain with regard to validating, integrating, and interpreting data. During the transition to lactation period, useful data are presented in the form of milk production and composition, milk Fourier-transform infrared (FTIR) wavelength absorbance, cow management records, and genomics, which have been employed to monitor postpartum onset of HYK. Attempts to predict postpartum HYK from test-day milk and performance variables incorporated into multiple linear regression models have demonstrated sufficient accuracy to monitor monthly herd prevalence; however, they lacked the sensitivity and specificity for individual cow diagnostics. Subsequent artificial neural network prediction models employing FTIR data and milk composition variables achieved 83 and 81% sensitivity and specificity for individual cow diagnostics. Although these results fail to reach the diagnostic goals of 90%, they are achieved without individual cow blood samples, which may justify acceptance of lower performance. The caveat is that these models depend on milk analysis, which is traditionally done every 4 weeks. This infrequent sampling allows for a single diagnostic sample for about half of the fresh cows. Benefits to farms are greatly improved if postpartum cows can be milk-tested weekly. Additionally, this allows for close monitoring of somatic cell count and may open the door for use of other herd health monitoring tools. Future improvements in these models may be achievable by maximizing sensitivity at the expense of specificity and may be most economical in disorders for which the cost of treatment is less than that of mistreating (e.g., HYK). Genomic predictions for HYK may be improved by incorporating genome-wide associated SNP and further utilized for precision management of HYK risk groups. Development and validation of HYK prediction models may provide producers with individual cow and herd-level management tools.

The effects of prepartum energy intake and peripartum rumen-protected choline supplementation on hepatic genes involved in glucose and lipid metabolism.

Nutritional interventions, either by controlling dietary energy (DE) or supplementing rumen-protected choline (RPC) or both, may mitigate negative postpartum metabolic health outcomes. A companion paper previously reported the effects of DE density and RPC supplementation on production and health outcomes. The objective of this study was to examine the effects of DE and RPC supplementation on the expression of hepatic oxidative, gluconeogenic, and lipid transport genes during the periparturient period. At 47 ± 6 d relative to calving (DRTC), 93 multiparous Holstein cows were randomly assigned in groups to dietary treatments in a 2 × 2 factorial of (1) excess energy (EXE) without RPC supplementation (1.63 Mcal of NEL/kg of dry matter; EXE-RPC); (2) maintenance energy (MNE) without RPC supplementation (1.40 Mcal of NEL/kg dry matter; MNE-RPC); (3) EXE with RPC supplementation (EXE+RPC); and (4) MNE with RPC supplementation (MNE+RPC). To achieve the objective of this research, liver biopsy samples were collected at -14, +7, +14, and +21 DRTC and analyzed for mRNA expression (n = 16/treatment). The interaction of DE × RPC decreased glucose-6-phosphatase and increased peroxisome proliferator-activated receptor α in MNE+RPC cows. Expression of cytosolic phosphoenolpyruvate carboxykinase was altered by the interaction of dietary treatments with reduced expression in EXE+RPC cows. A dietary treatment interaction was detected for expression of pyruvate carboxylase although means were not separated. Dietary treatment interactions did not alter expression of carnitine palmitoyltransferase 1A or microsomal triglyceride transfer protein. The 3-way interaction of DE × RPC × DRTC affected expression of carnitine palmitoyltransferase 1A, glucose-6-phosphatase, and peroxisome proliferator-activated receptor α and tended to affect cytosolic phosphoenolpyruvate carboxykinase. Despite previously reported independent effects of DE and RPC on production variables, treatments interacted to influence hepatic metabolism through altered gene expression.

Symposium review: One-carbon metabolism and methyl donor nutrition in the dairy cow.

The present review focuses on methyl donor metabolism and nutrition in the periparturient and lactating dairy cow. Methyl donors are involved in one-carbon metabolism, which includes the folate and Met cycles. These cycles work in unison to support lipid, nucleotide, and protein synthesis, as well as methylation reactions and the maintenance of redox status. A key feature of one-carbon metabolism is the multi-step conversion of tetrahydrofolate to 5-methyltetrahyrofolate. Homocysteine and 5-methyltetrahyrofolate are utilized by vitamin B12-dependent Met synthase to couple the folate and Met cycles and generate Met. Methionine may also be remethylated from choline-derived betaine under the action of betaine hydroxymethyltransferase. Regardless, Met is converted within the Met cycle to S-adenosylmethionine, which is universally utilized in methyl-group transfer reactions including the synthesis of phosphatidylcholine. Homocysteine may also enter the transsulfuration pathway to generate glutathione or taurine for scavenging of reactive oxygen metabolites. In the transition cow, a high demand exists for compounds with a labile methyl group. Limited methyl group supply may contribute to inadequate hepatic phosphatidylcholine synthesis and hepatic triglyceride export, systemic oxidative stress, and compromised milk production. To minimize the perils associated with methyl donor deficiency, the peripartum cow relies on de novo methylneogenesis from tetrahydrofolate. In addition, dietary supplementation of rumen-protected folic acid, vitamin B12, Met, choline, and betaine are potential nutritional approaches to target one-carbon pools and improve methyl donor balance in transition cows. Such strategies have merit considering research demonstrating their ability to improve milk production efficiency, milk protein synthesis, hepatic health, and immune response. This review aims to summarize the current understanding of folic acid, vitamin B12, Met, choline, and betaine utilization in the dairy cow. Methyl donor co-supplementation, fatty acid feeding strategies that may optimize methyl donor supplementation efficacy, and potential epigenetic mechanisms are also considered.

Postpartum supplementation with fermented ammoniated condensed whey altered nutrient partitioning to support hepatic metabolism.

Our previously published paper demonstrated that fermented ammoniated condensed whey (FACW) supplementation improved feed efficiency and metabolic profile in postpartum dairy cows. The objective of this study was to further explore the effects of FACW supplementation on liver triglyceride content, hepatic gene expression and protein abundance, and plasma biomarkers related to liver function, inflammation, and damage. Individually fed multiparous Holstein cows were blocked by calving date and randomly assigned to postpartum (1 to 45 d in milk, DIM) isonitrogenous treatments: control diet (n = 20) or diet supplemented with FACW (2.9% dry matter of diet as GlucoBoost; Fermented Nutrition, Luxemburg, WI, replacing soybean meal; n = 19). Liver biopsies were performed at 14 and 28 DIM for analysis of mRNA expression, protein abundance, and liver triglyceride content. There was marginal evidence for a reduction in liver triglyceride content at 14 DIM in FACW-supplemented cows compared with the control group. Cows supplemented with FACW had greater mRNA expression of glucose-6-phosphatase at 14 DIM relative to control. Supplementation with FACW increased mRNA expression of pyruvate carboxylase (PC), but did not alter cytosolic phosphoenolpyruvate carboxykinase (PCK1), resulting in a 2.4-fold greater PC:PCK1 ratio for FACW-supplemented cows compared with control. There was no evidence for a FACW effect on mRNA expression of propionyl-CoA carboxylase nor on mRNA expression or protein abundance of lactate dehydrogenase A or B. Cows supplemented with FACW had lower plasma urea nitrogen compared with control. Plasma l-lactate was greater for FACW-supplemented cows compared with control at 2 h before feeding time at 21 DIM. There was no evidence for altered expression of IL1B or IL10, or blood biomarkers related to liver function and damage. Greater glucose-6-phosphatase and PC gene expression, together with greater blood glucose and similar milk lactose output, suggests that FACW increased the supply of glucose precursors, resulting in greater gluconeogenesis between 3 and 14 DIM. Greater hepatic PC:PCK1 ratio, together with previously reported decreased plasma ß-hydroxybutyrate and the marginal evidence for lower liver triglyceride content at 14 DIM, suggests greater hepatic capacity for complete oxidation of fatty acids in FACW-supplemented cows compared with control. Overall, improvements in metabolite profile and feed efficiency observed with postpartum supplementation of FACW may be attributed to increased gluconeogenic and anaplerotic precursors, most likely propionate, due to modulated rumen fermentation.

Short communication: Effect of manipulating fatty acid profile on gluconeogenic gene expression in bovine primary hepatocytes.

During the peripartum period, dairy cows experience both an increase in circulating fatty acid (FA) profile and a change in circulating FA profile, which have been shown to alter regulation of gluconeogenic genes. The objective was to quantify gene expression of key enzymes involved in gluconeogenesis and FA transport into the mitochondria in primary hepatocytes in response to exposure to an FA mixture mimicking what is circulating in a transition dairy cow with or without enrichment of C16:0, C18:0, and C18:1. Primary hepatocytes were isolated from 4 Holstein bull calves 3 d of age (± standard deviation 2 d) and cultured. Twenty-four hours after plating, treatments were applied to the cells for 24-h incubation. Treatments consisted of (1) control (1% BSA), (2) 0.75 mM FA cocktail (3% C14:0, 27% C16:0, 23% C18:0, 31% C18:1, 8% C18:2, and 8% C18:3 to mimic the FA profile of dairy cattle at calving), (3) 0.90 mM FA cocktail, (4) 0.75 mM FA cocktail + 0.15 mM C16:0, (5) 0.75 mM FA cocktail + 0.15 mM C18:0, and (6) 0.75 mM FA cocktail + 0.15 mM C18:1. After harvest in Trizol (Life Technologies, Carlsbad, CA), samples were stored at -80°C until RNA extraction, purification, and reverse transcription. Abundance of mRNA was measured using quantitative real-time PCR. Expression of genes of interest [carnitine palmitoyltransferase 1A, pyruvate carboxylase, cytosolic phosphoenolpyruvate carboxykinase (PCK1), mitochondrial phosphoenolpyruvate carboxykinase, and glucose-6-phosphatase] was calculated relative to the average abundance of 2 reference genes (ribosomal protein L32 and glyceraldehyde 3-phosphate dehydrogenase), which were the most stable out of 3 tested. Data were analyzed using PROC MIXED (SAS version 9.4; SAS Institute, Cary, NC) with the fixed effect of treatment and calf in the random statement. The addition of FA compared with the 1% BSA treatment increased the expression of carnitine palmitoyltransferase 1A and cytosolic PCK1. Enrichment with individual FA did not further regulate pyruvate carboxylase or PCK1 beyond that achieved by the basal profile. These results suggest that shifts in circulating FA profile within a biological range, without a difference in the total FA concentration, have minimal effects on transcriptional regulation of hepatic gluconeogenic genes in primary bovine hepatocytes.

Postpartum supplementation of fermented ammoniated condensed whey improved feed efficiency and plasma metabolite profile.

Postpartum dietary supplementation of gluconeogenic precursors may improve the plasma metabolite profile of dairy cows, reducing metabolic disorders and improving lactation performance. The objective of this trial was to examine the effects of supplementation with fermented ammoniated condensed whey (FACW) postpartum on lactation performance and on profile of plasma metabolites and hormones in transition dairy cows. Individually fed multiparous Holstein cows were blocked by calving date and randomly assigned to control (2.9% dry matter of diet as soybean meal; n = 20) or FACW (2.9% dry matter of diet as liquid GlucoBoost, Fermented Nutrition, Luxemburg, WI; n = 19) dietary treatments. Treatments were offered from 1 to 45 d in milk (DIM). Cows were milked twice a day. Dry matter intake and milk yield were recorded daily and averaged weekly. Individual milk samples from 2 consecutive milkings were obtained once a week for component analysis. Rumen fluid was collected (n = 3 cows/treatment) at 4 time points per day at 7 and 21 DIM. Blood samples were collected within 1 h before feeding time for metabolite analysis and hyperketonemia diagnosis. Supplementation of FACW improved feed efficiency relative to control; this effect may be partially explained by a marginally significant reduction in dry matter intake from wk 3 to 7 for FACW-supplemented cows with no detected FACW-driven changes in milk yield, milk protein yield, and milk energy output compared with control. Also, there was no evidence for differences in intake of net energy for lactation, efficiency of energy use, energy balance, or body weight or body condition score change from calving to 45 DIM between treatments. Supplementation of FACW shifted rumen measures toward greater molar proportions of propionate and butyrate, and lesser molar proportions of acetate and valerate. Cows supplemented with FACW had greater plasma glucose concentrations in the period from 3 to 7 DIM and greater plasma insulin concentrations compared with control. Plasma nonesterified fatty acid and ß-hydroxybutyrate concentrations were decreased in cows supplemented with FACW compared with control cows in the period from 3 to 7 DIM. These findings indicate that FACW may have improved the plasma metabolite profile immediately postpartum in dairy cows. Additionally, supplementation of FACW resulted in improved feed efficiency as accessed by measures of milk output relative to feed intake.

Predicting blood ß-hydroxybutyrate using milk Fourier transform infrared spectrum, milk composition, and producer-reported variables with multiple linear regression, partial least squares regression, and artificial neural network.

Prediction of postpartum hyperketonemia (HYK) using Fourier transform infrared (FTIR) spectrometry analysis could be a practical diagnostic option for farms because these data are now available from routine milk analysis during Dairy Herd Improvement testing. The objectives of this study were to (1) develop and evaluate blood ß-hydroxybutyrate (BHB) prediction models using multivariate linear regression (MLR), partial least squares regression (PLS), and artificial neural network (ANN) methods and (2) evaluate whether milk FTIR spectrum (mFTIR)-based models are improved with the inclusion of test-day variables (mTest; milk composition and producer-reported data). Paired blood and milk samples were collected from multiparous cows 5 to 18 d postpartum at 3 Wisconsin farms (3,629 observations from 1,013 cows). Blood BHB concentration was determined by a Precision Xtra meter (Abbot Diabetes Care, Alameda, CA), and milk samples were analyzed by a privately owned laboratory (AgSource, Menomonie, WI) for components and FTIR spectrum absorbance. Producer-recorded variables were extracted from farm management software. A blood BHB ≥1.2 mmol/L was considered HYK. The data set was divided into a training set (n = 3,020) and an external testing set (n = 609). Model fitting was implemented with JMP 12 (SAS Institute, Cary, NC). A 5-fold cross-validation was performed on the training data set for the MLR, PLS, and ANN prediction methods, with square root of blood BHB as the dependent variable. Each method was fitted using 3 combinations of variables: mFTIR, mTest, or mTest + mFTIR variables. Models were evaluated based on coefficient of determination, root mean squared error, and area under the receiver operating characteristic curve. Four models (PLS-mTest + mFTIR, ANN-mFTIR, ANN-mTest, and ANN-mTest + mFTIR) were chosen for further evaluation in the testing set after fitting to the full training set. In the cross-validation analysis, model fit was greatest for ANN, followed by PLS and MLR. Diagnostic strength after cross-validation was poorest for MLR and was similar for ANN and PLS. Models that used mTest + mFTIR variables performed marginally better than models that used only mFTIR or mTest variables. These results suggest that blood BHB prediction models that use mFTIR + mTest variables may be useful additions to existing HYK diagnostic and management programs.

Technical note: Validation of the BHBCheck blood ß-hydroxybutyrate meter as a diagnostic tool for hyperketonemia in dairy cows.

Accurate cow-side blood ß-hydroxybutyrate (BHB) detection meters are valuable tools for rapid diagnosis of hyperketonemia. The main objective of this study was to compare the blood BHB measured in whole blood by the BHBCheck meter (PortaCheck, Moorestown, NJ) to a previously validated meter, Precision Xtra meter (Abbott Laboratories, Abbott Park, IL) and a colorimetric laboratory assay. Samples (n = 426) were collected from postpartum primiparous and multiparous Holstein cows (n = 79 cows) enrolled in 1 of 2 experiments (Exp) with different sampling schedules (Exp 1: n = 39 cows, 58 samples; Exp 2: n = 40 cows, 368 samples). In both Exp, whole-blood samples were collected from the coccygeal vessels after morning milking, before morning feeding. Blood samples were used immediately for BHB quantification via the BHBCheck meter and the Precision Xtra meter. Blood was also collected into evacuated tubes containing no additive (Exp 1) or potassium oxalate/sodium fluoride (Exp 2), which were centrifuged for serum or plasma separation and stored at -20°C for subsequent analysis. Laboratory quantification of BHB concentration was done by the BHB LiquiColor Assay (EKF Diagnostics-Stanbio, Boerne, TX; certified for serum and plasma). Data were analyzed by UNIVARIATE, CORR, FREQ, REG, and LOGISTIC procedures of SAS 9.4 (SAS Institute Inc., Cary, NC). Within this sample set, average parity was 3.3 lactations and DIM was 14 d. The proportion of samples classified as hyperketonemia (BHB ≥1.2 mmol/L) was 25, 28, and 31% as determined by the colorimetric assay, BHBCheck meter, and Precision Xtra meter, respectively. The correlation for BHBCheck meter BHB concentration compared with the colorimetric assay concentrations was r = 0.96, with a sensitivity of 91% and specificity of 93%. Correlation, sensitivity, and specificity of the Precision Xtra meter concentrations were 0.97, 98%, and 92%, respectively. Bland-Altman plots demonstrated minimal bias for both meters. Area under the receiver operator characteristic curve suggests adequate diagnostic accuracy of both meters. Overall, accuracy, sensitivity, and specificity of the BHBCheck meter was similar to the Precision Xtra meter and laboratory assay, indicating the BHBCheck meter is appropriate for use as a cow-side diagnostic test for hyperketonemia in dairy cows.

Predicting hyperketonemia by logistic and linear regression using test-day milk and performance variables in early-lactation Holstein and Jersey cows.

Although cowside testing strategies for diagnosing hyperketonemia (HYK) are available, many are labor intensive and costly, and some lack sufficient accuracy. Predicting milk ketone bodies by Fourier transform infrared spectrometry during routine milk sampling may offer a more practical monitoring strategy. The objectives of this study were to (1) develop linear and logistic regression models using all available test-day milk and performance variables for predicting HYK and (2) compare prediction methods (Fourier transform infrared milk ketone bodies, linear regression models, and logistic regression models) to determine which is the most predictive of HYK. Given the data available, a secondary objective was to evaluate differences in test-day milk and performance variables (continuous measurements) between Holsteins and Jerseys and between cows with or without HYK within breed. Blood samples were collected on the same day as milk sampling from 658 Holstein and 468 Jersey cows between 5 and 20 d in milk (DIM). Diagnosis of HYK was at a serum ß-hydroxybutyrate (BHB) concentration ≥1.2 mmol/L. Concentrations of milk BHB and acetone were predicted by Fourier transform infrared spectrometry (Foss Analytical, Hillerød, Denmark). Thresholds of milk BHB and acetone were tested for diagnostic accuracy, and logistic models were built from continuous variables to predict HYK in primiparous and multiparous cows within breed. Linear models were constructed from continuous variables for primiparous and multiparous cows within breed that were 5 to 11 DIM or 12 to 20 DIM. Milk ketone body thresholds diagnosed HYK with 64.0 to 92.9% accuracy in Holsteins and 59.1 to 86.6% accuracy in Jerseys. Logistic models predicted HYK with 82.6 to 97.3% accuracy. Internally cross-validated multiple linear regression models diagnosed HYK of Holstein cows with 97.8% accuracy for primiparous and 83.3% accuracy for multiparous cows. Accuracy of Jersey models was 81.3% in primiparous and 83.4% in multiparous cows. These results suggest that predicting serum BHB from continuous test-day milk and performance variables could serve as a valuable diagnostic tool for monitoring HYK in Holstein and Jersey herds.