Prolonged, low-grade inflammation in the first week of lactation: Associations with mineral, protein, and energy balance markers, and milk yield, in a clinically healthy Jersey cow cohort

Systemic inflammation in early lactation and its relation to the cows' oxidative and metabolic status, productive and reproductive performance, and activity

A dysregulated inflammatory response contributes to the occurrence of disorders in cows during the transition period from pregnancy to lactation. However, a detailed characterization of clinically healthy cows that exhibit an enhanced inflammatory response during this critical period remains incomplete. In this experiment, a total of 99 individual transition dairy cows and 109 observations (18 cows monitored in 2 consecutive lactations), submitted to similar transition management were involved to evaluate the relationship between elevated an inflammatory response and metabolic and oxidative status, as well as transition outcomes. Blood was taken at -7, 3, 6, 9, and 21 DIM, and concentrations of metabolic parameters (glucose, β-hydroxybutyric acid, nonesterified fatty acids [NEFA], insulin, IGF-1, and fructosamine) were analyzed. Additionally, oxidative parameters (proportion of oxidized glutathione to total glutathione in red blood cells, the activity of glutathione peroxidase [GPx] and superoxide dismutase, concentrations of malondialdehyde, and oxygen radical absorbance capacity) and acute phase proteins (APP) including haptoglobin (Hp), serum amyloid A (SAA) and albumin-to-globulin ratio (A:G) were determined in the blood at 21 DIM. The 3 APP parameters were used to group clinically healthy cows into 2 categories through k-medoids clustering (i.e., a group showing an acute phase response, APR; n = 39) and a group not showing such a response (i.e., non-APR; n = 50). Diseased cases (n = 20) were handled in a separate group. Lower SAA and Hp concentrations as well as higher A:G were observed in the non-APR group, although for Hp, differences were observed from the APR group and not from the diseased group. Only 1 of the 5 oxidative parameters differed between the groups, with the non-APR group exhibiting lower GPx activity compared with the diseased group. The non-APR group showed the highest IGF-1 levels among the 3 groups and and lower NEFA concentrations compared with the diseased groups. Cows in the diseased group also showed reduced dry matter intake and milk yield compared with clinically healthy cows, regardless of their inflammatory status. Moreover, the APR group exhibited temporarily lower activity levels compared with the non-APR group. These findings highlight that cows with a lower inflammatory status after 21 DIM exhibited better metabolic health characteristics and productive performance, as well as activity levels. Nevertheless, the detrimental effects of a higher inflammatory status in the absence of clinical symptoms are still relatively limited.

Genomic prediction of blood biomarkers of metabolic disorders in Holstein cattle using parametric and nonparametric models

BACKGROUND

Metabolic disturbances adversely impact productive and reproductive performance of dairy cattle due to changes in endocrine status and immune function, which increase the risk of disease. This may occur in the post-partum phase, but also throughout lactation, with sub-clinical symptoms. Recently, increased attention has been directed towards improved health and resilience in dairy cattle, and genomic selection (GS) could be a helpful tool for selecting animals that are more resilient to metabolic disturbances throughout lactation. Hence, we evaluated the genomic prediction of serum biomarkers levels for metabolic distress in 1353 Holsteins genotyped with the 100K single nucleotide polymorphism (SNP) chip assay. The GS was evaluated using parametric models best linear unbiased prediction (GBLUP), Bayesian B (BayesB), elastic net (ENET), and nonparametric models, gradient boosting machine (GBM) and stacking ensemble (Stack), which combines ENET and GBM approaches.

RESULTS

The results show that the Stack approach outperformed other methods with a relative difference (RD), calculated as an increment in prediction accuracy, of approximately 18.0% compared to GBLUP, 12.6% compared to BayesB, 8.7% compared to ENET, and 4.4% compared to GBM. The highest RD in prediction accuracy between other models with respect to GBLUP was observed for haptoglobin (hapto) from 17.7% for BayesB to 41.2% for Stack; for Zn from 9.8% (BayesB) to 29.3% (Stack); for ceruloplasmin (CuCp) from 9.3% (BayesB) to 27.9% (Stack); for ferric reducing antioxidant power (FRAP) from 8.0% (BayesB) to 40.0% (Stack); and for total protein (PROTt) from 5.7% (BayesB) to 22.9% (Stack). Using a subset of top SNPs (1.5k) selected from the GBM approach improved the accuracy for GBLUP from 1.8 to 76.5%. However, for the other models reductions in prediction accuracy of 4.8% for ENET (average of 10 traits), 5.9% for GBM (average of 21 traits), and 6.6% for Stack (average of 16 traits) were observed.

CONCLUSIONS

Our results indicate that the Stack approach was more accurate in predicting metabolic disturbances than GBLUP, BayesB, ENET, and GBM and seemed to be competitive for predicting complex phenotypes with various degrees of mode of inheritance, i.e. additive and non-additive effects. Selecting markers based on GBM improved accuracy of GBLUP.

Observational study on the associations between milk yield, composition, and coagulation properties with blood biomarkers of health in Holstein cows

The considerable increase in the production capacity of individual cows owing to both selective breeding and innovations in the dairy sector has posed challenges to management practices in terms of maintaining the nutritional and metabolic health status of dairy cows. In this observational study, we investigated the associations between milk yield, composition, and technological traits and a set of 21 blood biomarkers related to energy metabolism, liver function or hepatic damage, oxidative stress, and inflammation or innate immunity in a population of 1,369 high-yielding Holstein-Friesian dairy cows. The milk traits investigated in this study included 4 production traits (milk yield, fat yield, protein yield, daily milk energy output), 5 traits related to milk composition (fat, protein, casein, and lactose percentages and urea), 11 milk technological traits (5 milk coagulation properties and 6 curd-firming traits). All milk traits (i.e., production, composition, and technological traits) were analyzed according to a linear mixed model that included the days in milk, the parity order, and the blood metabolites (tested one at a time) as fixed effects and the herd and date of sampling as random effects. Our findings revealed that milk yield and daily milk energy output were positively and linearly associated with total cholesterol, nonesterified fatty acids, urea, aspartate aminotransferase, γ-glutamyl transferase, total bilirubin, albumin, and ferric-reducing antioxidant power, whereas they were negatively associated with glucose, creatinine, alkaline phosphatase, total reactive oxygen metabolites, and proinflammatory proteins (ceruloplasmin, haptoglobin, and myeloperoxidase). Regarding composition traits, the protein percentage was negatively associated with nonesterified fatty acids and β-hydroxybutyrate (BHB), while the fat percentage was positively associated with BHB, and negatively associated with paraoxonase. Moreover, we found that the lactose percentage increased with increasing cholesterol and albumin and decreased with increasing ceruloplasmin, haptoglobin, and myeloperoxidase. Milk urea increased with an increase in cholesterol, blood urea, nonesterified fatty acids, and BHB, and decreased with an increase in proinflammatory proteins. Finally, no association was found between the blood metabolites and milk coagulation properties and curd-firming traits. In conclusion, this study showed that variations in blood metabolites had strong associations with milk productivity traits, the lactose percentage, and milk urea, but no relationships with technological traits of milk. Specifically, increasing levels of proinflammatory and oxidative stress metabolites, such as ceruloplasmin, haptoglobin, myeloperoxidase, and total reactive oxygen metabolites, were shown to be associated with reductions in milk yield, daily milk energy output, lactose percentage, and milk urea. These results highlight the close connection between the metabolic and innate immunity status and production performance. This connection is not limited to specific clinical diseases or to the transition phase but manifests throughout the entire lactation. These outcomes emphasize the importance of identifying cows with subacute inflammatory and oxidative stress as a means of reducing metabolic impairments and avoiding milk fluctuations.

Association of time to metritis diagnosis with circulating concentration of metabolites, minerals, and haptoglobin in Jersey cows

Our objective was to investigate the association of early metritis [EMET, diagnosed at <5 d in milk (DIM)] and late metritis (LMET, diagnosed at ≥5 DIM) with circulating concentrations of energy metabolites, minerals, and haptoglobin (Hp) throughout the first 14 d postpartum. A total of 379 purebred Jersey cows were enrolled in a prospective cohort study from a single herd in west Texas. Cows were examined for metritis using the Metricheck device (Simcro Ltd.) at 4, 7, and 10 DIM. Cows identified by farm employees as possible metritis cases were also evaluated for metritis. Blood samples were collected for analysis of concentrations of Ca, Mg, and glucose at DIM 1 through 5, 7, 10, and 14. Albumin, urea, fructosamine, free fatty acids (FFA), creatinine, and β-hydroxybutyrate (BHB) were analyzed at DIM 3, 5, 7, 10, and 14, and Hp at DIM 1 through 5 and 7. Data were analyzed using the MIXED and PHREG procedures of SAS (SAS Institute Inc.). A series of mixed general linear models accounting for repeated measures were fitted to the data. The independent variables metritis [no metritis (NMET), EMET, and LMET], DIM of analyte assessment, and parity were forced in all models. Multivariable Cox proportional hazard models were built to assess the risk of pregnancy and culling within 150 DIM. The overall metritis incidence was 26.9% (EMET = 49; LMET = 53; NMET = 277). Average concentrations of glucose, Mg, and urea were not associated with metritis. The associations of Ca, creatinine, BHB, and fructosamine with metritis were dependent on the DIM of analyte assessment. Cows categorized as EMET and LMET had, on average, lower albumin and fructosamine compared with NMET cows. Both EMET and LMET cows had, on average, greater BHB than NMET cows. A greater FFA concentration was only observed in cows diagnosed with EMET compared with NMET cows (EMET = 0.58, LMET = 0.52, NMET = 0.48 mmol/L). Additionally, circulating Hp concentration was greater for LMET and EMET compared with NMET cows, and EMET cows had greater Hp compared with LMET cows (EMET = 1.15; LMET = 1.00; NMET = 0.84). In conclusion, several blood biomarkers were temporally associated with early versus late metritis diagnosis in postpartum Jersey cows. No meaningful differences were observed in production, reproduction, or culling between EMET and LMET cows. These results suggest that cows with EMET undergo a more severe degree of inflammation and negative energy balance compared with NMET cows.

Prediction of detailed blood metabolic profile using milk infrared spectra and machine learning methods in dairy cattle

The adoption of preventive management decisions is crucial to dealing with metabolic impairments in dairy cattle. Various serum metabolites are known to be useful indicators of the health status of cows. In this study, we used milk Fourier-transform mid-infrared (FTIR) spectra and various machine learning (ML) algorithms to develop prediction equations for a panel of 29 blood metabolites, including those related to energy metabolism, liver function/hepatic damage, oxidative stress, inflammation/innate immunity, and minerals. For most traits, the data set comprised observations from 1,204 Holstein-Friesian dairy cows belonging to 5 herds. An exception was represented by β-hydroxybutyrate prediction, which contained observations from 2,701 multibreed cows pertaining to 33 herds. The best predictive model was developed using an automatic ML algorithm that tested various methods, including elastic net, distributed random forest, gradient boosting machine, artificial neural network, and stacking ensemble. These ML predictions were compared with partial least squares regression, the most commonly used method for FTIR prediction of blood traits. Performance of each model was evaluated using 2 cross-validation (CV) scenarios: 5-fold random (CV_r) and herd-out (CV_h). We also tested the best model's ability to classify values precisely in the 2 extreme tails, namely, the 25th (Q25) and 75th (Q75) percentiles (true-positive prediction scenario). Compared with partial least squares regression, ML algorithms achieved more accurate performance. Specifically, elastic net increased the R² value from 5% to 75% for CV_r and 2% to 139% for CV_h, whereas the stacking ensemble increased the R² value from 4% to 70% for CV_r and 4% to 150% for CV_h. Considering the best model, with the CV_r scenario, good prediction accuracies were obtained for glucose (R² = 0.81), urea (R² = 0.73), albumin (R² = 0.75), total reactive oxygen metabolites (R² = 0.79), total thiol groups (R² = 0.76), ceruloplasmin (R² = 0.74), total proteins (R² = 0.81), globulins (R² = 0.87), and Na (R² = 0.72). Good prediction accuracy in classifying extreme values was achieved for glucose (Q25 = 70.8%, Q75 = 69.9%), albumin (Q25 = 72.3%), total reactive oxygen metabolites (Q25 = 75.1%, Q75 = 74%), thiol groups (Q75 = 70.4%), total proteins (Q25 = 72.4%, Q75 = 77.2.%), globulins (Q25 = 74.8%, Q75 = 81.5%), and haptoglobin (Q75 = 74.4%). In conclusion, our study shows that FTIR spectra can be used to predict blood metabolites with relatively good accuracy, depending on trait, and are a promising tool for large-scale monitoring.