Influence of Days in Milk and Parity on Milk and Blood Fatty Acid Concentrations, Blood Metabolites and Hormones in Early Lactation Holstein Cows

Canonical Correlation of Milk Composition Parameters and Blood Biomarkers in High-Producing Dairy Cows During Different Lactation Stages

This study explores milk composition and blood markers in cows across lactation stages. Holstein cows were divided into four groups: beginning of lactation (BL; n = 21), peak of lactation (PL; n = 21), middle of lactation (ML; n = 21), and end of lactation (EL; n = 20). Blood (1 × 15 mL) and milk samples (1 × 100 mL) were collected for biomarker analysis. Blood chemistry profiles were determined using a clinical chemistry analyser, and milk lactose, fat, and protein levels (%) were determined using an infrared absorbance analyser. Minerals (Ca, P, and Mg) in milk were determined by atomic absorption spectrometry after mineralizing the samples. Glucose was higher in the EL group than in the BL group (p < 0.01), whereas D-beta-hydroxybutyrate (D-BHB) was higher in the BL group than in the PL and ML groups (p < 0.001). Cholesterol was higher in the PL, ML, and EL groups than in the BL group (p < 0.001). Gamma-glutamyl transferase was increased in the PL group compared to the BL group. Phosphorus levels were lower in the PL than in the BL group, whereas protein levels were higher in the EL than in the PL group. Spearman and partial correlation analysis showed several significant associations between the observed variables. Using canonical correlation analysis were identified three significant correlations (rc1 = 0.853; rc2 = 0.823; rc3 = 0.739). The main canonical correlation identified blood TG and milk urea as the strongest variables. According to the canonical loading, the biomarkers TG, Mg, urea, cholesterol, and alkaline phosphatase (U1) are the primary variables associated with milk parameters (V1), specifically with milk urea, milk Mg and P, protein, and lactose.

The Characteristics of Milk Fatty Acid Profile Predicted by Fourier-Transform Mid-Infrared Spectroscopy (FT-MIRS) in Chinese Holstein Cows

Fatty acid is an important factor affecting the nutritional quality of milk. In this study, we collected and assessed 78,086 milk samples from 12,065 Chinese Holstein cows from 11 farms in Northern China from November 2019 to September 2022. The contents of eight fatty acid groups were predicted using FT-MIRS-based models. The contents of TFAs, SFAs, UFAs, MUFAs, PUFAs, and LCFAs in milk reached the highest at 96-125 DIM, and SCFA and MCFA contents reached the highest at 276-305 DIM. With the increase in somatic cell score, the contents of various fatty acid groups in milk gradually decreased, and the nutritional value of milk and flavor of dairy products gradually deteriorated. The contents of high-quality fatty acids in milk, particularly UFAs and MUFAs, were significantly higher in the non-pregnant state than in the pregnant state. However, SCFA and MCFA contents exhibited the opposite pattern. Our findings provided valuable information on the content and distribution range of fatty acid groups in milk from Chinese Holstein cows. Further analysis is warranted to explore the breeding of Chinese Holstein cows providing milk with abundant beneficial fatty acids.

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.

Intraday and interday variations of milk fatty acids in dairy cows in early and late lactation

The composition of milk fatty acid (FA) was determined using Fourier-transform mid-infrared spectroscopy, which enables the rapid measurement of many samples. Milk FA is one indicator supporting the management of dairy cows and herds. This study aimed to determine an appropriate sampling method for milk FA in a practical farm condition based on intraday and interday variations in milk FA composition during early and late lactation stages. Milk samples were collected in the morning (07:00-08:00 h) and afternoon (16:30-17:30 h) for five consecutive days during early and late lactation. Within the day, de novo FA as the total FA basis was higher in the morning than in the afternoon, whereas preformed FA as the total FA basis was lower in the morning than in the afternoon. The weighted averages of milk FA composition according to milk yield collected in the morning and afternoon were significantly different between cows in early and late lactation; however, these were not significantly different among the consecutive five sampling days in each period. It was concluded that milk samples collected in the morning and afternoon for 1 day are suitable for milk FA determination. These results provide basic data for determining precise sampling methods for practical farms.

Differentiating between metabolic health statuses in Simmental cows and describing related milk fatty acids and relevant associated factors

The aim of this observational study was to examine differences in milk fatty acid (FA) concentrations for different metabolic health statuses and for associated factors-specifically to examine with which FA concentrations an increased risk for developing a poor metabolic adaptation syndrome (PMAS) was associated. During weekly visits over 51 wk, blood samples were collected from cows between 5 and 50 days in milk. The farmer collected corresponding milk samples from all voluntary milkings. The analysis was performed on n = 2,432 samples from n = 553 Simmental cows. The observations were assigned to five different cow types (healthy, clever, athletic, hyperketonemic, and PMAS, representing five metabolic health statuses), based on the thresholds of 0.7 mmol/L, 1.2 mmol/L, and 1.4 for the concentrations of β-hydroxybutyrate and nonesterified fatty acids and for the milk fat-to-protein ratio, respectively. Linear regression models using the predictor variables cow type, parity, week of lactation, and milk yield as fixed effects were developed using a stepwise forward selection to test for significant associations of predictor variables regarding FA concentrations in milk. There was a significant interaction term found between PMAS cows and parity compared to healthy cows for C18:1 (P < 0.001) and for C18:0 (P < 0.01). It revealed higher concentrations for PMAS in primiparous and multiparous cows compared to healthy cows, the slope being steeper for primiparous cows. Further, an interaction term was found between PMAS cows and milk yield compared to healthy cows and milk yield for C16:0 (P < 0.05), revealing a steeper slope for the decrease of C16:0 concentrations with increasing milk yield for PMAS compared to healthy cows. The significant associations and interaction terms between cow type, parity, week of lactation, and milk yield as predictor variables and C16:0, C18:0, and C18:1 concentrations suggest excellent opportunities for cow herd health screening during the early postpartum period.

Effect of Parity, Body Condition Score at Calving, and Milk Yield on the Metabolic Profile of Gyr Cows in the Transition Period

This study aimed to evaluate the effects of parity, body condition score (BCS) at calving, and milk yield on the metabolic profile of Gyr (Zebu) cows. Healthy cows in late pregnancy were grouped according to parity (primiparous, biparous, and multiparous); to BCS scale at calving (high-HBCS and normal-NBCS); and to milk yield (high-HP and moderate-MP production). BCS was assessed, and blood samples were collected on -21, -7, 0, 7, 21, and 42 days relative to parturition. The concentrations of non-esterified fatty acids (NEFA), beta-hydroxybutyrate (BHB), cholesterol, glucose, total protein (TP), albumin, total calcium (Ca), phosphorus (P), and magnesium (Mg); and activities of aspartate aminotransferase and gamma-glutamyltransferase were measured. Data were analyzed by two-way repeated measures ANOVA. The frequencies of high lipomobilization, subclinical ketosis, subclinical hypocalcemia (SCH), and the occurrence of diseases during early lactation were established. Regardless of grouping, NEFA, BHB, and cholesterol increased during early lactation; glucose showed higher values at calving; TP and albumin were higher at 21 and 42 DIM; and Ca, P, and Mg were lower at calving. Parity had little effect on the metabolic profile, HBCS did not differ from NBCS cows, and HP did not differ from MP cows in most metabolites. High lipomobilization in early lactation and SCH at calving were the most common imbalances but were not related to postpartum diseases. High-yielding Gyr cows have a balanced metabolic profile during the transition period, with few biologically relevant effects of parity, BCS at parturition, or milk yielded.

Integrating on-farm and genomic information improves the predictive ability of milk infrared prediction of blood indicators of metabolic disorders in dairy cows

BACKGROUND

Blood metabolic profiles can be used to assess metabolic disorders and to evaluate the health status of dairy cows. Given that these analyses are time-consuming, expensive, and stressful for the cows, there has been increased interest in Fourier transform infrared (FTIR) spectroscopy of milk samples as a rapid, cost-effective alternative for predicting metabolic disturbances. The integration of FTIR data with other layers of information such as genomic and on-farm data (days in milk (DIM) and parity) has been proposed to further enhance the predictive ability of statistical methods. Here, we developed a phenotype prediction approach for a panel of blood metabolites based on a combination of milk FTIR data, on-farm data, and genomic information recorded on 1150 Holstein cows, using BayesB and gradient boosting machine (GBM) models, with tenfold, batch-out and herd-out cross-validation (CV) scenarios.

RESULTS

The predictive ability of these approaches was measured by the coefficient of determination (R²). The results show that, compared to the model that includes only FTIR data, integration of both on-farm (DIM and parity) and genomic information with FTIR data improves the R² for blood metabolites across the three CV scenarios, especially with the herd-out CV: R² values ranged from 5.9 to 17.8% for BayesB, from 8.2 to 16.9% for GBM with the tenfold random CV, from 3.8 to 13.5% for BayesB and from 8.6 to 17.5% for GBM with the batch-out CV, and from 8.4 to 23.0% for BayesB and from 8.1 to 23.8% for GBM with the herd-out CV. Overall, with the model that includes the three sources of data, GBM was more accurate than BayesB with accuracies across the CV scenarios increasing by 7.1% for energy-related metabolites, 10.7% for liver function/hepatic damage, 9.6% for oxidative stress, 6.1% for inflammation/innate immunity, and 11.4% for mineral indicators.

CONCLUSIONS

Our results show that, compared to using only milk FTIR data, a model integrating milk FTIR spectra with on-farm and genomic information improves the prediction of blood metabolic traits in Holstein cattle and that GBM is more accurate in predicting blood metabolites than BayesB, especially for the batch-out CV and herd-out CV scenarios.

Differentiation of Subclinical Ketosis and Liver Function Test Indices in Adipose Tissues Associated With Hyperketonemia in Postpartum Dairy Cattle

Past studies suggested that during early lactation and the transition period, higher plasma growth hormone (GH) levels in subclinical ketosis (SCK) might involve the initiation of body adipose tissues mobilization, resulting in metabolic disorders in ruminants particularly hyperketonemia. The upregulated GH mRNA expression in adipose tissue may take part in the adipolysis process in SCK-affected cows that paves a way for study further. This study aimed to characterize the plasma levels of GH, β-hydroxybutyrate acid (BHBA) and non-esterified fatty acid (NEFA) and glucose (GLu) in ketotic cows and healthy control (CON) cows; to measure the liver function test (LFT) indices in ketotic and healthy CON cows, and finally the quantitative real-time PCR (qRT-PCR) assay of candidate genes expressed in adipose tissues of ketotic and healthy CON cows during 0 to 7 week postpartum. Three experiments were conducted. Experiment-1 involved 21 Holstein cows weighing 500–600 kg with 2–5 parities. Results showed that GH, BHBA, and NEFA levels in ketotic cows were significantly higher and the GLu level significantly lower. Pearson's correlation analysis revealed a significant positive correlation of GH with BHBA, NEFA, and GLu in ketotic and healthy CON cows. In experiment-2, dynamic monitoring of LFT indices namely, alanine aminotransferase (ALT), aspartate aminotransferase (AST), γ-glutamyl transpeptidase (GGT), total bilirubin (TBIL), direct bilirubin (DBIL), total protein (TP), albumin (ALB), globulin (GLOB) and albumin/globulin (A/G) were examined. The TBIL, DBIL, and GGT indices were significantly higher in ketotic cows and TP was significantly lower. In experiment-3, mRNA expression levels of GHR and peroxisome-proliferator-activated receptor alpha (PPARα) genes in adipose tissue were significantly upregulated in ketotic cows. However, the mRNA expression of insulin-like growth factor-I (IGF-1), insulin-like growth factor-I receptor (IGF-1R), and sterol regulatory element-binding protein-1c (SREBP-1c) genes in adipose tissue were downregulated in ketotic cows. Our study concluded that during postpartum, higher plasma GH levels in SCK cows might involve the initiation of body adipose tissue mobilization, resulting in hyperketonemia.

Welfare Assessment: Correspondence Analysis of Welfare Score and Hematological and Biochemical Profiles of Dairy Cows in Sardinia, Italy

The need for animal welfare definition and assessment is increasing worldwide, and several studies have been conducted to help fill the knowledge gaps regarding the welfare of cattle. However, further studies are needed to provide valid synthetized measures for welfare evaluation. The aim of this study was to assess the welfare status of 16 Sardinian dairy cattle farms, based on the developed Animal Welfare and Biosecurity Evaluation checklist (AWB-EF) and the corresponding hematological, biochemical, and electrophoretic profiles of these animals. Considering the AWB-EF as gold standard, blood samples were collected from 230 Holstein breed dairy cattle, aged between 3 and 8 years, out of the periparturient period, and with no clinical signs of specific pathologies. Principal Component (PC) and correlation analyses were performed to simplify phenomena interpretation and assess positive/negative associations. Four PCs were able to explain 76% of the total variability, and six laboratory parameters were strongly associated with the AWB-EF score (Spearman's correlation coefficient ≥ 0.40, p-Value < 0.05), reflecting the real health status of the animals. Given the complexity of animal welfare as a multidimensional concept and the need to include both animal-based and non-based measures in welfare evaluation, the present work represents a sound basis for future evaluation and veterinary health planning.