Novel prediction models for hyperketonemia using bovine milk Fourier-transform infrared spectroscopy.

Metabolic diseases driven by negative energy balance in dairy cattle contribute to reduced milk production, increased disease incidence, culling, and death. Cow side tests for negative energy balance markers are available but are labor-intensive. Milk sample analysis using Fourier transform infrared spectroscopy (FTIR) allows for sampling numerous cows simultaneously. FTIR prediction models have moderate accuracy for hyperketonemia diagnosis (beta-hydroxybutyrate (BHB) ≥ 1.2 mmol/L). Most research using FTIR has focused on homogenous datasets and conventional prediction models, including partial least squares, linear discriminant analysis, and ElasticNet. Our objective was to evaluate more diverse modeling options, such as deep learning, gradient boosting machine models, and model ensembles for hyperketonemia classification. We compiled a sizable, heterogeneous dataset including milk FTIR and concurrent blood samples. Blood samples were tested for blood BHB, and wavenumber data was obtained from milk FTIR analysis. Using this dataset, we trained conventional prediction models and other options listed above. We demonstrate prediction model performance is similar for convolutional neural networks and ensemble models to simpler algorithm options. Results obtained from this study indicate that deep learning and model ensembles are potential algorithm options for predicting hyperketonemia in dairy cattle. Additionally, our results indicate hyperketonemia prediction models can be developed using heterogeneous datasets.

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.

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.

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.

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.

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.

Prediction of whole-genome risk for selection and management of hyperketonemia in Holstein dairy cattle.

Hyperketonemia (HYK), a common early postpartum health disorder characterized by elevated blood concentrations of ß-hydroxybutyrate (BHB), affects millions of dairy cows worldwide and leads to significant economic losses and animal welfare concerns. In this study, blood concentrations of BHB were assessed for 1,453 Holstein cows using electronic handheld meters at four time points between 5 and 18 days postpartum. Incidence rates of subclinical (1.2 ≤ maximum BHB ≤ 2.9 mmol/L) and clinical ketosis (maximum BHB ≥ 3.0 mmol/L) were 24.0 and 2.4%, respectively. Variance components, estimated breeding values, and predicted HYK phenotypes were computed on the original, square-root, and binary scales. Heritability estimates for HYK ranged from 0.058 to 0.072 in pedigree-based analyses, as compared to estimates that ranged from 0.071 to 0.093 when pedigrees were augmented with 60,671 single nucleotide polymorphism genotypes of 959 cows and 801 male ancestors. On average, predicted HYK phenotypes from the genome-enhanced analysis ranged from 0.55 mmol/L for first-parity cows in the best contemporary group to 1.40 mmol/L for fourth-parity cows in the worst contemporary group. Genome-enhanced predictions of HYK phenotypes were more closely associated with actual phenotypes than pedigree-based predictions in five-fold cross-validation, and transforming phenotypes to reduce skewness and kurtosis also improved predictive ability. This study demonstrates the feasibility of using repeated cowside measurement of blood BHB concentration in early lactation to construct a reference population that can be used to estimate HYK breeding values for genomic selection programmes and predict HYK phenotypes for genome-guided management decisions.