Days-in-Milk and Parity Affected Serum Biochemical Parameters and Hormone Profiles in Mid-Lactation Holstein Cows

Dynamic Changes in the Nutrient Digestibility, Rumen Fermentation, Serum Parameters of Perinatal Ewes and Their Relationship with Rumen Microbiota

Changes in physiological and biochemical parameters are crucial for the reproductive performance and health of perinatal ewes. This study investigated the temporal variations in feed intake, nutrient digestibility, serum parameters, and ruminal fermentation on days 21, 14, and 7 before lambing (Q21, Q14, and Q7) and days 3, 7, and 14 after lambing (H3, H7, and H14). The results showed that dry matter intake (DMI) and glucose (Glu) gradually decreased (p < 0.05) before lambing and increased (p < 0.05) after lambing. The digestibility of dry matter (DMD), crude protein (CPD), and acid detergent fiber (ADFD) increased (p < 0.05) before lambing, then decreased (p < 0.05) on day H3, and then increased (p < 0.05) on day H14. The rumen pH, NH3-N, and triglycerides (TG) gradually increased (p < 0.05) before lambing and were higher (p < 0.05) on day Q7 than after lambing. The concentrations of acetate, butyrate, and total volatile fatty acids (T-VFA) were lower (p < 0.05) on day Q7 than those on days Q21 and Q14, then increased (p < 0.05) after lambing. Total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C), and low-density lipoprotein cholesterol (LDL-C) concentrations gradually decreased (p < 0.05) in perinatal ewes. BHBA and NEFA concentrations were lower (p < 0.05) on day Q21 than those from days Q14 to H14. The rumen microbiota compositions were different (p < 0.05) in perinatal ewes, and g_Anaerovibrio, g_Lachnobacterium, and g_Schwartzia were positively correlated (p < 0.05) with DMI, Glu, acetate, propionate, and T-VFA, and negatively correlated (p < 0.05) with LDL-C. g_Bacillus was negatively correlated (p < 0.05) with DMI, Glu, acetate, propionate, butyrate, and T-VFA, but positively correlated (p < 0.05) with rumen pH and LDL-C. In summary, the DMI, nutrient digestibility, rumen fermentation, and serum parameters changed during the perinatal period of ewes, and the changes in DMI, serum glucose, acetate, propionate, and T-VFA were related to the rumen bacteria.

Combining genetic markers, on-farm information and infrared data for the in-line prediction of blood biomarkers of metabolic disorders in Holstein cattle

BACKGROUND

Various blood metabolites are known to be useful indicators of health status in dairy cattle, but their routine assessment is time-consuming, expensive, and stressful for the cows at the herd level. Thus, we evaluated the effectiveness of combining in-line near infrared (NIR) milk spectra with on-farm (days in milk [DIM] and parity) and genetic markers for predicting blood metabolites in Holstein cattle. Data were obtained from 388 Holstein cows from a farm with an AfiLab system. NIR spectra, on-farm information, and single nucleotide polymorphisms (SNP) markers were blended to develop calibration equations for blood metabolites using the elastic net (ENet) approach, considering 3 models: (1) Model 1 (M1) including only NIR information, (2) Model 2 (M2) with both NIR and on-farm information, and (3) Model 3 (M3) combining NIR, on-farm and genomic information. Dimension reduction was considered for M3 by preselecting SNP markers from genome-wide association study (GWAS) results.

RESULTS

Results indicate that M2 improved the predictive ability by an average of 19% for energy-related metabolites (glucose, cholesterol, NEFA, BHB, urea, and creatinine), 20% for liver function/hepatic damage, 7% for inflammation/innate immunity, 24% for oxidative stress metabolites, and 23% for minerals compared to M1. Meanwhile, M3 further enhanced the predictive ability by 34% for energy-related metabolites, 32% for liver function/hepatic damage, 22% for inflammation/innate immunity, 42.1% for oxidative stress metabolites, and 41% for minerals, compared to M1. We found improved predictive ability of M3 using selected SNP markers from GWAS results using a threshold of > 2.0 by 5% for energy-related metabolites, 9% for liver function/hepatic damage, 8% for inflammation/innate immunity, 22% for oxidative stress metabolites, and 9% for minerals. Slight reductions were observed for phosphorus (2%), ferric-reducing antioxidant power (1%), and glucose (3%). Furthermore, it was found that prediction accuracies are influenced by using more restrictive thresholds (-log10(P-value) > 2.5 and 3.0), with a lower increase in the predictive ability.

CONCLUSION

Our results highlighted the potential of combining several sources of information, such as genetic markers, on-farm information, and in-line NIR infrared data improves the predictive ability of blood metabolites in dairy cattle, representing an effective strategy for large-scale in-line health monitoring in commercial herds.

Metabolic Changes in Serum and Milk of Holstein Cows in Their First to Fourth Parity Revealed by Biochemical Analysis and Untargeted Metabolomics

The performance of dairy cows is closely tied to the metabolic state, and this performance varies depending on the number of times the cows have given birth. However, there is still a lack of research on the relationship between the metabolic state of Holstein cows and the performance of lactation across multiple parities. In this study, biochemical analyses and metabolomics studies were performed on the serum and milk from Holstein cows of parities 1-4 (H1, N = 10; H2, N = 7; H3, N = 9; H4, N = 9) in mid-lactation (DIM of 141 ± 4 days) to investigate the link between performance and metabolic changes. The results of the milk quality analysis showed that the lactose levels were highest in H1 (p = 0.036). The total protein content in the serum increased with increasing parity (p = 0.013). Additionally, the lipase activity was found to be lowest in H1 (p = 0.022). There was no difference in the composition of the hydrolyzed amino acids in the milk among H1 to H4. However, the free amino acids histidine and glutamate in the serum were lowest in H1 and highest in H3 (p < 0.001), while glycine was higher in H4 (p = 0.031). The metabolomics analysis revealed that 53 and 118 differential metabolites were identified in the milk and serum, respectively. The differential metabolites in the cows' milk were classified into seven categories based on KEGG. Most of the differential metabolites in the cows' milk were found to be more abundant in H1, and these metabolites were enriched in two impact pathways. The differential metabolites in the serum could be classified into nine categories and enriched in six metabolic pathways. A total of six shared metabolites were identified in the serum and milk, among which cholesterol and citric acid were closely related to amino acid metabolism in the serum. These findings indicate a significant influence of blood metabolites on the energy and amino acid metabolism during the milk production process in the Holstein cows across 1-4 lactations, and that an in-depth understanding of the metabolic changes that occur in Holstein cows during different lactations is essential for precision farming, and that it is worthwhile to further investigate these key metabolites that have an impact through controlled experiments.

The Relation between Hair-Cortisol Concentration and Various Welfare Assessments of Dutch Dairy Farms

Many protocols have been developed to assess farm animal welfare. However, the validity of these protocols is still subject to debate. The present study aimed to compare nine welfare assessment protocols, namely: (1) Welfare Quality^© (WQ), (2) a modified version of Welfare Quality (WQ Mod), which has a better discriminative power, (3) WelzijnsWijzer (Welfare Indicator; WW), (4) a new Welfare Monitor (WM), (5) Continue Welzijns Monitor (Continuous Welfare Monitor; CWM), (6) KoeKompas (Cow Compass; KK), (7) Cow Comfort Scoring System (CCSS), (8) Stall Standing Index (SSI) and (9) a Welfare Index (WI Tuyttens). In addition, a simple welfare estimation by veterinarians (Estimate vets, EV) was added. Rank correlation coefficients were calculated between each of the welfare assessment protocol scores and mean hair cortisol concentrations from 10 cows at 58 dairy farms spread over the Netherlands. Because it has been suggested that the hair cortisol level is related to stress, experienced over a long period of time, we expected a negative correlation between cortisol and the result of the welfare protocol scores. Only the simple welfare estimation by veterinarians (EV) (ρ = -0.28) had a poor, but significant, negative correlation with hair cortisol. This correlations, however, failed to reach significance after correction of p-values for multiple correlations. Most of the results of the different welfare assessment protocols had a poor, fair or strong positive correlation with each other, supporting the notion that they measure something similar. Additional analyses revealed that the modified Welfare Quality protocol parameters housing (ρ = -0.30), the new Welfare Monitor (WM) parameter health (ρ = -0.33), and milk yield (ρ = -0.33) showed negative correlations with cortisol. We conclude that because only five out of all the parameter scores from the welfare assessment protocols showed a negative, albeit weak, correlation with cortisol, hair cortisol levels may not provide a long term indicator for stress in dairy cattle, or alternatively, that the protocols might not yield valid indices for cow welfare.