Investigation of dairy cow performance in different udder health groups defined based on a combination of somatic cell count and differential somatic cell count

Genetic parameters for novel mastitis traits defined by combining test-day somatic cell score and differential somatic cell count in the first lactation of Japanese Holsteins

In this study, we aimed to improve current udder health genetic evaluations by addressing the limitations of monthly sampled somatic cell score (SCS) for distinguishing cows with robust innate immunity from those susceptible to chronic infections. The objectives were to (1) establish novel somatic cell traits by integrating SCS and the differential somatic cell count (DSCC), which represents the combined proportion of polymorphonuclear leukocytes and lymphocytes in somatic cells and (2) estimate genetic parameters for the new traits, including their daily heritability and genetic correlations with milk production traits and SCS, using a random regression test-day model (RRTDM). We derived 3 traits, termed ML_SCS_DSCC, SCS_4_DSCC_65_binary, and ML_SCS_DSCC_binary, by using milk loss (ML) estimates at corresponding SCS and DSCC levels, thresholds established in previous studies, and a threshold established from milk loss estimates, respectively. Data consisted of test-day records collected during January 2021 through March 2022 from 265 herds in Hokkaido, Japan. From these records, we extracted records between 7 to 305 d in milk (DIM) in the first lactation to fit the RRTDM. The model included the random effect of herd-test-day, the fixed effect of year-month, fixed lactation curves nested with calving age groups, and random regressions with Legendre polynomials of order 3 for additive genetic and permanent environmental effects. The analysis was performed using Gibbs sampling with Gibbsf90+ software. The averages (ranges) of the daily heritability estimates over lactation were 0.086 (0.075-0.095) for SCS, 0.104 (0.073-0.127) for ML_SCS_DSCC, 0.137 (0.014-0.297) for SCS_4_DSCC_65_binary, and 0.138 (0.115-0.185) for ML_SCS_DSCC_binary; the heritability curve for SCS_4_DSCC_65_binary was erratic. Genetic correlations within the trait decreased as the DIM interval widened, especially for those integrating DSCC, indicating that these traits should be analyzed using RRTDM rather than repeatability models. The averages (ranges) of genetic correlations with milk yield over lactation were 0.01 (-0.22 to 0.28) for SCS, -0.05 (-0.40 to 0.13) for ML_SCS_DSCC, -0.08 (-0.17 to 0.09) for SCS_4_DSCC_65_binary, and -0.08 (-0.22 to 0.27) for ML_SCS_DSCC_binary. Compared with SCS, the newly defined traits exhibited slightly stronger negative genetic correlations with milk yield. Especially in late lactation stages, the genetic correlation between ML_SCS_DSCC and milk yield was significantly below zero, with a posterior median of -0.40. Furthermore, the new traits showed positive correlations with SCS, having estimates varying from 0.68 to 0.85 for ML_SCS_DSCC, 0.14 to 0.47 for SCS_4_DSCC_65_binary, and 0.61 to 0.66 for ML_SCS_DSCC_binary, depending on DIM. Considering that ML_SCS_DSCC and ML_SCS_DSCC_binary have relatively high heritability (compared with SCS) and favorable genetic correlations with milk production traits and SCS, their incorporation into breeding programs appears promising. Nevertheless, their genetic relationships with (sub)clinical mastitis require further investigation.

Pathogen and severity-dependent immune responses in bovine mastitis: highlight the dynamics of differential somatic cell count

Immune responses in bovine clinical mastitis (CM) probably differ depending on the causative pathogen and disease severity. The observational study aimed to investigate whether both factors are associated with the dynamics of immune indicators, including somatic cell score (SCS), white blood cell count (WBC), serum albumin/globulin (A/G) ratio, and differential somatic cell count (DSCC). We collected blood and milk samples 0, 3, 5, 7, 14, and 21 days after CM occurred in 38 cows, and grouped the cases (n=49) by disease severity and pathogen. We analyzed data using a linear mixed model considering the effects of pathogens and severity, calculated estimated-marginal means for indicators at each time point, and compared the means between groups. The dynamics of WBC varied depending on both pathogen and severity. WBC changed drastically in either severe or coliform-caused CM, slightly elevated in streptococcal mastitis, but unchanged in staphylococcal mastitis. This possibly relates to the deficiency in innate immune response toward staphylococci. The A/G ratio also changed depending on severity, as it dropped sharply only in severe CM. We observed a non-linear relationship between DSCC and SCS, possibly due to mammary epithelial cells shedding in milk when CM occurred. When cows recovering from Streptococcus dysgalatiae mastitis, DSCC decreased while SCS remained high, suggesting a healing process requiring more macrophages. Our results demonstrate that both the severity and pathogen are associated with immune responses in CM, providing insights into mastitis pathogenesis.

A First Investigation into the Use of Differential Somatic Cell Count as a Predictor of Udder Health in Sheep

Differential somatic cell count (DSCC), the percentage of somatic cell count (SCC) due to polymorphonuclear leukocytes (PMNs) and lymphocytes (LYMs), is a promising effective diagnostic marker for dairy animals with infected mammary glands. Well-explored in dairy cows, DSCC is also potentially valid in sheep, where clinical and subclinical mastitis outbreaks are among the principal causes of culling. We pioneered the application of DSCC in dairy ewes by applying receiver-operating characteristic (ROC) curve analysis to define the most accurate thresholds to facilitate early discrimination of sheep with potential intramammary infection (IMI) from healthy animals. We tested four predefined SCC cut-offs established in previous research. Specifically, we applied SCC cut-offs of 265 × 103 cells/mL, 500 × 103 cells/mL, 645 × 103 cells/mL, and 1000 × 103 cells/mL. The performance of DSCC as a diagnostic test was assessed by examining sensitivity (Se), specificity (Sp), positive predictive value (PPV), negative predictive value (NPV), and area under curve (AUC) analyses. The designated threshold value for DSCC in the detection of subclinical mastitis is established at 79.8%. This threshold exhibits Se and Sp of 0.84 and 0.81, accompanied by an AUC of 0.88. This study represents the inaugural exploration of the potential use of DSCC in sheep's milk as an early indicator of udder inflammation.

Subclinical Mastitis from Streptococcus agalactiae and Prototheca spp. Induces Changes in Milk Peptidome in Holstein Cattle

Early detection of bovine subclinical mastitis may improve treatment strategies and reduce the use of antibiotics. Herein, individual milk samples from Holstein cows affected by subclinical mastitis induced by S. agalactiae and Prototheca spp. were analyzed by untargeted and targeted mass spectrometry approaches to assess changes in their peptidome profiles and identify new potential biomarkers of the pathological condition. Results showed a higher amount of peptides in milk positive on the bacteriological examination when compared with the negative control. However, the different pathogens seemed not to trigger specific effects on the milk peptidome. The peptides that best distinguish positive from negative samples are mainly derived from the most abundant milk proteins, especially from β- and αs1-casein, but also include the antimicrobial peptide casecidin 17. These results provide new insights into the physiopathology of mastitis. Upon further validation, the panel of potential discriminant peptides could help the development of new diagnostic and therapeutic tools.

Estimating the nonlinear interaction between somatic cell score and differential somatic cell count on milk production by parity using generalized additive models

This observational study aimed to use somatic cell score (SCS) and differential somatic cell count (DSCC), the combined proportion of polymorphonuclear leukocytes and lymphocytes in somatic cells, to investigate how mastitis affected milk production. Using generalized additive models, we analyzed 50,618 test-day records from 8,081 lactations from 7,912 cows in 197 herds between January 2021 and March 2022 to estimate the nonlinear interaction between SCS and DSCC, and the effects of lactation stages and seasons on milk yield, milk component percentages, and milk component yields by parity of cows. The results show that the interaction between SCS and DSCC on these traits was significant, nonlinear, and complex. When DSCC was high, the negative effects of SCS were minimal, even when SCS reached 8 (i.e., 3,200,000 somatic cells/mL). Cows with high DSCC could have milk yields similar to healthy cows, implying that these cows may have been in the early stages of mastitis and that the milk yield had yet to be affected. Contrastingly, when DSCC was low, milk loss due to high SCS was drastic, especially for cows in third or later lactations, whose milk yield could reduce from more than 35 kg/d to less than 15 kg/d (-59.9%). This tremendous milk loss in high-parity cows was likely due to their higher milk yield and higher risks of chronic mastitis. High SCS and low DSCC also led to a pronounced change in milk composition. The decrease in the percentage of lactose can be directly related to the damage of inflammation to the mammary gland, while the increase in fat and protein percentages was more attributable to the concentration effect resulting from the reduced milk yield. Compared with analyses based on categorized SCS and DSCC values, modeling these 2 indices directly helps us more precisely assess mastitis effects on milk yield and milk composition. For efficient milk production, our results indicate that we should prevent high-parity cows from entering a state of high SCS and low DSCC.

Usefulness of the total and differential somatic cell count based udder health group concept for evaluating herd management practices and udder health in dairy herds

Subclinical mastitis and associated economic losses are a steady challenge in the dairy industry. The combination of the well-established somatic cell count (SCC) parameter and the new differential SCC (DSCC) opens up the possibility to categorise cows into four different udder health groups (UHG) based on results from a single milk recording/dairy herd improvement (DHI) test: UHG A: healthy/normal, ≤ 200,000 cells/mL and DSCC ≤ 65 %; B: suspicious, ≤ 200,000 cells/mL and DSCC > 65 %; C: (subclinical) mastitis, > 200,000 cells/mL and DSCC > 65 %; D: chronic/persistent mastitis, > 200,000 cells/mL and DSCC ≤ 65 %. The objectives of this study were to investigate 1) herd management practises among herds in different UHG categories and 2) herd performance parameters depending on the proportion of cows in UHG A. A total number of 41 herds in Styria, Austria, and Thuringia, Germany, were visited and interviewed for the first part of the study. The herds were categorised into 3 UHG categories depending on the proportion of cows in UHG A: I = >65 %; II = 55-65 %; and III = <55 %. Those with good udder health and best herd performance (+9 % milk yields, +11 % longevity, -35 % antibiotic treatments) applied distinct preventive measures, in particular excellent cubicle management and early antibiotic treatment (P < 0.05 each). However, preventive measures were applied to a lower extent in other herds. Herds were categorised differently using the UHG concept compared to SCC alone as the UHG-based categorisation allowed to clearer distinguish herds with medium-good from those with good udder health. A total number of 129,812 regular milk recording/DHI test day results of 890 Austrian and 183 German herds was used for the second part of the study. Results revealed a trend of increasing daily production as proportions of cows in UHG A increase. In conclusion, the UHG concept allowed clearer distinction of herds with good, medium-good, and poor udder health and could be used to promote practises leading to better animal health, less antibiotic treatments, and higher milk quality.

Pathogen Detection via Quantitative PCR in Milk of Healthy Cows Collected Using Different Sampling Protocols

In this study we evaluated the prevalence of pathogens detected via quantitative PCR (qPCR) in milk from apparently healthy cows to identify the most common etiological agents present in Italian dairy farms. Milk samples were collected using a sterile protocol at quarter-level (3239 samples, 822 cows) and a conventional protocol at udder level as composite milk from the functional quarters of each cow (5464 samples, 5464 cows). The qPCR commercial kit detected Mycoplasma bovis, Mycoplasma spp., Staphylococcus aureus, coagulase-negative staphylococci (CNS), Streptococcus agalactiae, Streptococcus dysgalactiae, Streptococcus uberis, Prototheca spp., Escherichia coli, Klebsiella spp., Enterococcus spp. and Lactococcus lactis ssp. lactis as well as DNA from the penicillin resistance β-lactamase gene from staphylococci. The prevalence of specific DNA was calculated based on its presence or absence in the samples, factoring in both the sampling protocols and herds. Regardless of the sampling protocol used, the most frequently detected pathogens were CNS (26.6% in sterile and 13.9% in conventional protocol) and Streptococcus uberis (9.6% and 16.5%, respectively). These results underscore the necessity for pathogen-specific interventions at the farm level to enhance the udder health of dairy cows via management recommendations.

Udder health-related traits in cow milk: phenotypic variability and effect on milk yield and composition

The milk differential somatic cell count (DSCC) has been proposed in recent years as a mean by which to better monitor the udder health status (UHS) in dairy cows. Milk DSCC is the amount of polymorphonuclear neutrophils and lymphocytes contributing to the total somatic cell count (SCC) and can be determined on a routine basis in individual milk samples subjected to official analysis. In the present study, 522 865 milk test-day records of 77 143 cows were scrutinised to identify factors affecting the variability of both DSCC and SCC in Holstein Friesian, Jersey, Simmental and Rendena cows through linear mixed models. The fixed effects were breed, parity, lactation stage, sampling season, and all the first-order interactions of breed. Cow and herd-test-date were considered as random. Subsequently, four UHS groups were created (1: SCC ≤ 200 000 cells/mL and DSCC ≤ 65%; 2: SCC ≤ 200 000 cells/mL and DSCC > 65%; 3: SCC > 200 000 cells/mL and DSCC > 65%; 4: SCC > 200 000 cells/mL and DSCC ≤ 65%) to compare milk yield and quality. Milk SCS and DSCC differed across lactation, parity, sampling season and breed. In particular, Simmental cows had the lowest SCC and Jersey the lowest DSCC. Depending on the breed, UHS affected daily milk yield and composition to a different extent. The UHS group 4, i.e. the one grouping test-day records with high SCC and low DSCC, presented the lowest estimate of milk yield and lactose content no matter the breeds. Our findings support that udder health-related traits (SCS and DSCC) are useful information to improve udder health at individual cow and herd levels. Moreover, the combination of SCS and DSCC is useful to monitor milk yield and composition.

Total and Differential Somatic Cell Count in Italian Local Cattle Breeds: Phenotypic Variability and Effect on Milk Yield and Composition

Milk differential somatic cell count (DSCC) represents the percentage of polymorphonuclear neutrophils and lymphocytes out of the total somatic cell count (SCC) and has been proposed in recent years as a proxy for udder health in dairy cows. We investigated phenotypic factors affecting SCC and DSCC using 3978 records of 212 Alpine Grey and 426 Burlina cows farmed in Northern Italy. The linear mixed model accounted for the fixed effects of breed, parity, lactation stage, sampling season, and first-order interactions of breed with the other effects. Cow, herd-test-date nested within breed were random. Subsequently, four udder health status groups (UHS) were created by combining SCC and DSCC to assess the UHS impact on milk yield and quality. DSCC was greater in Alpine Grey (66.2 ± 0.8%) than Burlina cows (63.2 ± 0.6%) and, similarly to SCC, it increased with days in milk and parity regardless of breed. Milk yield and composition were affected by UHS in both breeds. These results suggest that also udder health of local breeds can be monitored on a large scale through SCC and DSCC for reduction in biodiversity loss and increased farm profitability. However, in addition to milk data, the introduction of mastitis recording and monitoring plans is advisable.

Physiological Aspects of Milk Somatic Cell Count in Small Ruminants—A Review

The aim of this review was to focus on the physiological aspects of milk somatic cell count (SCC) in small ruminants (SM). The SCC is an important component naturally present in milk and is generally used as an indicator of milk quality and udder health in milk producing ruminants. SCC contains the following cells: polymorphonuclear neutrophils (PMN), macrophages, lymphocytes, and many milk epithelial (MEC) cells, cell fragments, and cytoplasmic particles/vesicles. PMN (40–80%) represent the major cell type in milk in healthy uninfected goats, whereas the macrophages (45–88%) are the major cell type in sheep’s milk. However, dairy goats and sheep have an apocrine secretory system that produces cytoplasmic cellular particles/vesicles and large numbers of cell fragments, resulting in the physiological SCC limit being exceeded. It is obvious that the SCC level in milk of SM can be affected by various influencing factors, such as milk fraction, breed, stage of lactation, parity, type of birth, milking system, and others. An increase in the SCC above the physiological level not only indicates an udder or general health problem but reduces milk production, changes the milk composition, and hence affects milk processing. Moreover, the milking machine plays an important role in maintaining udder health in SM and stable SCC at physiological levels in the milk obtained. So far, there are no healthy or pathological physiological SCC levels defined in SM milk. Furthermore, a differential cell count (DCC) or even a high resolution DCC (HRDCC), which were recently developed for cattle milk, could also help in SM to gain deeper insight into the immunology of the mammary gland and find biomarkers to assess udder health. In conclusion, SCC is an indication of udder health or exposure of the udder to infectious agents or mechanical stress and should therefore always be considered a warning sign.

Blood neutrophil extracellular traps: a novel target for the assessment of mammary health in transition dairy cows

Background

Mammary health is important for transition dairy cows and has been well recognized to exert decisive effects on animal welfare. However, the factors influencing mammary health are still unclear. Differential somatic cell count (DSCC) could reflect the mastitis risk since it is the percentage of neutrophils plus lymphocytes in total somatic cells and could be reflective of mammary health of dairy cows. This work aimed to investigate the assessment and prognosis of the health of transition cows based on blood neutrophil extracellular traps (NETs).

Results

Eighty-four transition Holstein dairy cows were selected. The serum was sampled in all the animals at week 1 pre- and postpartum, and milk was sampled at week 1 postpartum. Based on the DSCC in milk at week 1, cows with lower (7.4% ± 4.07%, n = 15) and higher (83.3% ± 1.21%, n = 15) DSCCs were selected. High DSCC cows had higher levels of red blood cell counts (P < 0.05), hemoglobin (P = 0.07), and hematocrit (P = 0.05), higher concentrations of serum oxidative variables [(reactive oxygen species (P < 0.05), malondialdehyde (P < 0.05), protein carbonyl (P < 0.05), and 8-hydroxy-2-deoxyguanosine (P = 0.07)], higher levels of serum and milk NETs (P < 0.05) and blood-milk barrier indicators, including serum β-casein (P = 0.05) and milk immunoglobulin G2 (P = 0.09), than those of low DSCC cows. In addition, lower concentrations of serum nutrient metabolites (cholesterol and albumin) (P < 0.05) and a lower level of serum deoxyribonuclease I (P = 0.09) were observed in high DSCC cows than in low DSCC cows. Among the assessments performed using levels of the three prepartum serum parameters (NETs, deoxyribonuclease I and β-casein), the area under the curve (0.973) of NETs was the highest. In addition, the sensitivity (1.00) and specificity (0.93) were observed for the discrimination of these cows using NETs levels with a critical value of 32.2 ng/mL (P < 0.05).

Conclusions

The formation of NETs in blood in transition dairy cows may damage the integrity of the blood-milk barrier and thereby increase the risk for mastitis in postpartum cows.

Association between differential somatic cell count and California Mastitis Test results in Holstein cattle

The California Mastitis Test (CMT) has been used to estimate total somatic cell count (SCC) levels in milk; however, milk with similar SCC levels occasionally shows inconsistent CMT results, which limits the use of the CMT. This observational study aimed to investigate how differential cell counts in milk influence the CMT in Holstein cattle through the novel parameters differential somatic cell count (DSCC) and macrophage proportion (MAC). We performed the CMT on d 0, 3, 5, 7, 14, and 21 after identifying mastitis, and simultaneously measured SCC, DSCC, and MAC at the quarter level. We followed 58 mastitis events occurring in 41 cows and obtained 307 quarter-level records after data cleaning. We transformed SCC to somatic cell score (SCS) and MAC to its logarithm to fit the normal distribution and analyzed the data using the cumulative logit mixed model. Results showed that both an increase in SCS (odds ratio: 3.66, 95% confidence interval: 2.89-4.64) and the logarithm of MAC (odds ratio: 4.35, 95% confidence interval: 1.91-9.91) can contribute to a higher CMT score. During the healing process of mastitis, MAC tends to increase as SCC decreases; thus, even samples with low SCC can cause positive CMT reactions. We recommend that practitioners avoid making treatment decisions based on the CMT alone. We also noted that the CMT is sensitive to subclinical mastitis with high MAC, hence it could be considered an alternative to detecting high MAC (chronic) mastitis.

A machine learning approach using partitioning around medoids clustering and random forest classification to model groups of farms in regard to production parameters and bulk tank milk antibody status of two major internal parasites in dairy cows

Fasciola hepatica and Ostertagia ostertagi are internal parasites of cattle compromising physiology, productivity, and well-being. Parasites are complex in their effect on hosts, sometimes making it difficult to identify clear directions of associations between infection and production parameters. Therefore, unsupervised approaches not assuming a structure reduce the risk of introducing bias to the analysis. They may provide insights which cannot be obtained with conventional, supervised methodology. An unsupervised, exploratory cluster analysis approach using the k–mode algorithm and partitioning around medoids detected two distinct clusters in a cross-sectional data set of milk yield, milk fat content, milk protein content as well as F. hepatica or O. ostertagi bulk tank milk antibody status from 606 dairy farms in three structurally different dairying regions in Germany. Parasite–positive farms grouped together with their respective production parameters to form separate clusters. A random forests algorithm characterised clusters with regard to external variables. Across all study regions, co–infections with F. hepatica or O. ostertagi, respectively, farming type, and pasture access appeared to be the most important factors discriminating clusters (i.e. farms). Furthermore, farm level lameness prevalence, herd size, BCS, stage of lactation, and somatic cell count were relevant criteria distinguishing clusters. This study is among the first to apply a cluster analysis approach in this context and potentially the first to implement a k–medoids algorithm and partitioning around medoids in the veterinary field. The results demonstrated that biologically relevant patterns of parasite status and milk parameters exist between farms positive for F. hepatica or O. ostertagi, respectively, and negative farms. Moreover, the machine learning approach confirmed results of previous work and shed further light on the complex setting of associations a between parasitic diseases, milk yield and milk constituents, and management practices.

Using High-Resolution Differential Cell Counts (HRDCCs) in Bovine Milk and Blood to Monitor the Immune Status over the Entire Lactation Period

Differential cell counts in milk offer a deeper insight into the immunology of the mammary gland and might even provide information about the systemic health status of a dairy cow. Consequently, their potential as a diagnostic method to identify biomarkers has been a subject of research for quite some time. The objective of our study was to closely monitor the immune status of eight healthy dairy cows throughout their whole lactation. For this, high-resolution differential cell counts in milk and blood were determined by means of flow cytometry, which included 10 subpopulations of the 3 main populations of immune cells and their viability. Milk and blood samples were taken twice a week in the first 100 days after calving and once a week during the remaining lactation period: in total, 55 (52–57) blood and 55 (52–57) milk samples per animal. In addition, six well-established routine laboratory biomarkers, i.e., haptoglobin, calcium, and different metabolic parameters (non-esterified fatty acids, β-hydroxybutyric acid, bilirubin, and glutamate dehydrogenase), were analyzed in all blood samples. Furthermore, a standard differential blood cell count was performed on all blood samples. We found substantial differences between cell count progressions in the blood and milk. The distribution of cell populations in the blood remained mostly stable throughout the lactation, albeit at different individual levels. Several cell populations in the milk showed a noticeable dynamic over time, which caused a separation of different lactation stages in clustering analyses. Gamma delta T cells and CD4+ T cells in the milk stood out as they showed characteristic fluctuations during the course of lactation as well as minor changes in the case of inflammation. The determination of a differential cell count has the potential to be a sensitive diagnostic and prognostic tool in bovine milk. Further studies need to show to what extent the method is suitable for routine diagnostics and how to deal with animal-specific differences.

Impact of udder infections on biochemical composition of milk in context of pesticides exposure

Background and Aim:

Environmental contaminants such as pesticides have shown immunomodulatory effects that can make animals highly susceptible to pathogenic invasion. The current work aims to study the incidence of udder infections in a single dairy herd of 160 cows in Qalyoubia Governorate, in relation to the potential intoxication of dairy cattle with organochlorine (OCs) pesticides. The study also aims to investigate the impact of udder infections on milk composition.

Materials and Methods:

The dairy herd was screened for udder infections using the California mastitis test and measurement of somatic cell count (SCC), followed by bacteriological and molecular analysis. In parallel, the milk samples were also tested for residues of 15 OCs compounds using gas chromatographic analysis.

Results:

The examined herd showed a high prevalence of mastitis (37.5%) and Mycoplasma was identified as the main bacterial pathogen. OCs residues were detected in milk of 45 cows out of 160 with a higher incidence in mastitic (43.3%) than in healthy cows (19%). Further, the biochemical analysis of milk showed a significant drop in major electrolytes combined with a significant rise in blood-borne electrolytes (Na and Cl) and total protein. This was more extreme in the case of Mycoplasam mastitis compared to non-Mycoplasma mastitis. In addition, Mycoplasma mastitic milk revealed a high level of malondialdehyde associated with reduced antioxidant enzymes (glutathione peroxidase, superoxide dismutase and catalase), compared to non-Mycoplasma mastitis.

Conclusion:

Mycoplasma mastitis was shown to be associated with increased SCC and, in turn, appeared significantly correlated with increased biochemical changes in milk, indicating the serious impact of Mycoplasma mastitis on the dairy industry. Our data also show a strong correlation between increased SCC and biochemical changes in milk, suggesting that tested biochemical parameters might serve as potential biomarkers for the early detection of mastitis. The study also suggested a potential relationship between poisoning with OCs and susceptibility to bacterial udder infections. However, further studies are required to examine the immune status of a dairy herd in relation to the level of OCs in cow’s blood, as well as the water sources used, grass forage and soil.

Peripartal Rumen-Protected L-Carnitine Manipulates the Productive and Blood Metabolic Responses in High-Producing Holstein Dairy Cows

This study aimed to monitor the effect of including rumen-protected L-carnitine (Carneon 20 Rumin-Pro, Kaesler Nutrition GmbH, Cuxhaven, Germany) in the transition diet on the productive and metabolic responses of multiparous high-producing Holstein dairy cows. Thirty-two multiparous cows were allocated in a completely randomized design to receive the same diet plus 60 g fat prill containing 85% palmitic acid (control, n = 16) or 100 g rumen-protected L-carnitine (RLC, n = 16); at 28 days before expected calving until 28 days in milk (DIM). Fat prill was included in the control diet to balance the palmitic acid content of both experimental diets. Milk production over the 28 DIM for the control and RLC groups was 46.5 and 47.7 kg, respectively. Milk fat content tended to increase upon rumen-protected L-carnitine inclusion (p = 0.1). Cows fed rumen-protected L-carnitine had higher fat- and energy-corrected milk compared with the control group. Pre- and post-partum administration of L-carnitine decreased both high- and low-density lipoprotein concentrations in peripheral blood of post-partum cows. The results of this study indicated that the concentration of triglycerides and beta-hydroxybutyrate was not significantly different between the groups, whereas the blood non-esterified fatty acid concentration was markedly decreased in cows supplemented with L-carnitine. Animals in the RLC group had a significant (p < 0.05) lower blood haptoglobin concentration at 7 and 14 DIM than the control. Animals in the RLC group had a lower concentration of blood enzymes than those of the control group. The mRNA abundance of Toll-like receptors 4, cluster of differentiation 14, and myeloid differential protein 2 did not significantly change upon the supplementation of L-carnitine in the transition diet. In summary, the dietary inclusion of RLC improved dairy cow's performance during the early lactation period. Greater production, at least in part, is driven by improved energy utilization efficiency and enhanced metabolic status in animals during the periparturient period.

The untargeted lipidomic profile of quarter milk from dairy cows with subclinical intramammary infection by non-aureus staphylococci

This observational study determined the lipidome of cow milk during subclinical intramammary infection (IMI) by non-aureus staphylococci (NAS), also defined as coagulase-negative staphylococci, using an untargeted approach. Among the pathogens causing bovine IMI, NAS have become the most frequently isolated bacteria from milk samples. Although the application of system biology approaches to mastitis has provided pivotal information by investigating the transcriptome, proteome, peptidome, and metabolome, the milk lipidome during mammary gland inflammation remains undisclosed. To cover this gap, we determined the milk lipidome of 17 dairy cows with IMI caused by NAS (NAS-IMI), and we compared the results with those of healthy quarter milk from 11 cows. The lipidome was determined following a liquid chromatography-quadrupole time-of-flight mass spectrometry approach. Sixteen subclasses of lipids were identified in both groups of animals. From 2,556 measured lipids, the abundance of 597 changed more than 10-fold in quarter milk with NAS-IMI compared with healthy quarters. The results demonstrate the influence of NAS-IMI on the milk lipidome, implying significant changes in lipid species belonging to the family of triacylglycerols and sphingomyelins, and contribute to the understanding of inflammatory processes in the bovine udder, highlighting potential novel biomarkers for improving mastitis diagnostics.

Associations between different udder health groups defined based on a combination of total and differential somatic cell count and the future udder health status of dairy cows

Mastitis, in particular in its subclinical form, which may spread unnoticeable within a herd, continues to be a major challenge in the dairy industry. Somatic cell count (SCC) is a broadly used proxy for subclinical mastitis. The recently introduced Differential SCC (DSCC) representing the combined proportion of polymorphonuclear neutrophils and lymphocytes as a percentage of total SCC, can be used in combination with SCC to categorise cows into four different udder health groups (UHG) depending on actual test day results: UHG A: healthy/normal, ≤200,000 cells/mL and DSCC ≤65 %; B: suspicious, ≤200,000 cells/mL and DSCC >65 %; C: (subclinical) mastitis, >200,000 cells/mL and DSCC >65 %; D: chronic/persistent mastitis, >200,000 cells/mL and DSCC ≤65 %. The objective of our study was to investigate to what extent the UHG aid in determining different statuses of cows: I) leaving herd before next test day, II) having >200,000 cells/mL at the next test day, and III) having ≤200,000 cells/mL at the next 3 test days. Multivariable logistic regression analysis was used to evaluate these statuses based on routinely generated dairy herd improvement (DHI) data from Austria, China, Estonia, Germany, and Spain. Cows in groups C (odds ratio (OR): 2.13, 95 % confidence interval (CI): 1.95-2.34) and, particularly, D (OR: 3.91, 95 % CI: 3.31-4.62) were significantly more likely to leave herds compared to cows in group A. Late-lactating cows indicated the highest likelihood (OR: 16.03, 95 % CI: 14.44-17.81) to leave herds in our analysis. Interestingly, we found that cows in UHG B had significantly higher odds (OR: 2.77, 95 % CI: 2.58-2.98) to have >200,000 cells/mL at the next test day compared to cows in group A. As anticipated, cows in UHG B (OR: 0.40, 95 % CI: 0.38-0.42), C (OR: 0.08, 95 % CI: 0.07-0.09), and D (OR: 0.16, 95 % CI: 0.14-0.19) each were significantly less likely to have ≤200,000 cells/mL at the next 3 test days compared to cows in group A. Above described results are an example from Germany, but the same trends could be seen across all countries considered in our study. In conclusion, our findings illustrate that the UHG concept reveals additional valuable information about udder health and culling based a single test day over working with SCC only. Actual decisions in day-to-day farm management that could be taken were not investigated here and need to be further explored.

Hyperketonemia Predictions Provide an On-Farm Management Tool with Epidemiological Insights

Simple Summary

In dairy cows, the transition to lactation period is metabolically challenging. Elevated blood ketone bodies, known as hyperketonemia or ketosis, is a postpartum metabolic disorder that is associated with negative energy balance, greater comorbidity risk, and decreased milk production. Research to understand the etiology of hyperketonemia has highlighted risk factors and unfavorable outcomes; however, analysis of real-world data is valuable for determining the outcomes across a region. Dairy herd improvement data from herds with diverse size and production were analyzed to determine potential risk factors for and production outcomes of hyperketonemia in the Midwest region (US). Cows predicted to have hyperketonemia had greater previous lactation dry period length, somatic cell count, and dystocia, which may represent risk factors for ketosis. Cows with predicted hyperketonemia had lower milk yield and milk protein but greater milk fat and somatic cell count in the current lactation. Culling rate within 60d of calving, days open, and artificial inseminations were all greater in cows predicted to have hyperketonemia. Prevalence of hyperketonemia decreased linearly in herds with greater rolling herd average milk yield. This work demonstrates the impact of hyperketonemia on production variables which underscores the importance on continued work to reduce hyperketonemia prevalence.

Abstract

Prediction of hyperketonemia (HYK), a postpartum metabolic disorder in dairy cows, through use of cow and milk data has allowed for high-throughput detection and monitoring during monthly milk sampling. The objective of this study was to determine associations between predicted HYK (pHYK) and production parameters in a dataset generated from routine milk analysis samples. Data from 240,714 lactations across 335 farms were analyzed with multiple linear regression models to determine HYK status. Data on HYK or disease treatment was not solicited. Consistent with past research, pHYK cows had greater previous lactation dry period length, somatic cell count, and dystocia. Cows identified as pHYK had lower milk yield and protein percent but greater milk fat, specifically greater mixed and preformed fatty acids (FA), and greater somatic cell count (SCC). Differential somatic cell count was greater in second and fourth parity pHYK cows. Culling (60d), days open, and number of artificial inseminations were greater in pHYK cows. Hyperketonemia prevalence decreased linearly in herds with greater rolling herd average milk yield. This research confirms previously identified risk factors and negative outcomes associated with pHYK and highlights novel associations with differential SCC, mixed FA, and preformed FA across farm sizes and production levels.

Dynamics of somatic cell count (SCC) and differential SCC during and following intramammary infections

Somatic cell count is frequently used as an indicator of intramammary infections (IMI) in dairy cattle worldwide. The newly introduced differential SCC (DSCC) can potentially contribute to detection of IMI. The purpose of this study was to investigate the dynamics of SCC and DSCC after IMI. We used a data set with monthly samples from 2 Danish dairy herds through 1 yr, using bacterial culture to identify IMI. The dynamics of SCC and DSCC with regard to IMI were assessed at quarter level following new IMI with each of 3 defined pathogen groups, major, minor, or "other" pathogens, using general additive models. Both SCC and DSCC increased after IMI, with a more pronounced increase if major or other pathogens were detected compared with minor pathogens. We found that DSCC increased after IMI with other pathogens in both herds and, in herd 2, after IMI caused by major and minor pathogens. We also estimated the duration of increased SCC and DSCC when they exceeded a threshold, done separately for each pathogen group. Major pathogens had the longest-lasting effect in both herds for both SCC and DSCC. We conclude that the magnitude and duration of response of SCC and DSCC to IMI differs between herds and causative pathogens.

Differential Somatic Cell Count: Value for Udder Health Management

Intramammary infection (IMI) can cause mastitis, which is one of the costliest and most prevalent diseases in dairy cattle herds. Somatic cell count (SCC) is a well-established parameter to indicate IMI, and it represents the total count of immune cells in the milk. The differential somatic cell count (DSCC) has also long been suggested to indicate IMI, but no machine was available until recently to provide this parameter automatically. Two new machines have recently been introduced to measure the milk DSCC as an additional indicator of IMI. Here we provide insights about the DSCC measured by these two machines and the value it may provide for udder health management, based on the available literature. We also provide perspectives for future research to investigate potential value in using the DSCC to improve udder health.