Ketone bodies - causes and effects of their increased presence in cows' body fluids: A review

Evaluation of the Use of Different Sensitive Cardiac Biomarkers in Determining Myocardial Damage in Cows With Subclinical and Clinical Ketoses

BACKGROUND

It is unclear whether the increase in BHBA concentration damages the myocardium in ketotic cows. Measurement of troponin is the gold standard in myocardial injury.

OBJECTIVE

The objective of this study was to explore the impact of elevated β-hydroxybutyrate (BHBA) concentrations on myocardial damage in cows diagnosed with subclinical ketosis (SCK) and clinical ketosis (CK). Additionally, the study aimed to elucidate the relationship between heart-type fatty acid-binding protein 3 (H-FABP 3) and high-sensitivity cardiac troponin I (hs-cTnI) concentrations, as well as their correlation with various biochemical parameters that assess myocardial and metabolic health.

METHODS

The animal material used in this study consisted of 30 postpartum Holstein cows, aged between 2 and 5 years. The study group comprised 10 cows with SCK, 10 with CK and 10 cows in the control group (CG).

RESULTS

The concentration of hs-cTnI in cows with CK was significantly higher compared to both the CG (p < 0.005) and the SCK group (p < 0.019). Similarly, the H-FABP 3 concentration in the CK and SCK groups was significantly elevated compared to the CG (p < 0.001). The creatinine kinase myocardial band (CK-MB) levels were significantly higher in cows with CK compared to the CG (p < 0.010). Furthermore, the aspartate aminotransferase (AST) levels in the CK group were significantly elevated compared to the CG (p < 0.011). A significant positive correlation was observed between BHBA concentration and the levels of both hs-cTnI and H-FABP 3 in ketotic cows. Additionally, significant correlations were found between various biochemical parameters (alanine aminotransferase [ALT], AST, CK-MB and phosphorus) and the concentrations of H-FABP 3 and hs-cTnI.

CONCLUSIONS

This study showed that high BHBA concentrations in ketosis cows may indirectly cause myocardial damage. However, future studies need to investigate the relationship between oxidative stress and changes in antioxidant concentrations and troponin.

Cullin 3 mitigates nonesterified fatty acid-induced oxidative stress in mammary epithelial cells: Involvement of BCL2/BECN1 and autophagy

High nonesterified fatty acid (NEFA) concentrations in cows with clinical ketosis lead to metabolic dysfunction in mammary cells, resulting in oxidative stress. Studies have shown that autophagy is impaired in the mammary glands of ketotic cows, and enhancing autophagy mitigates oxidative stress in these animals. Cullin 3 (CUL3), an E3 ubiquitin ligase, is integral for maintaining cellular homeostasis, particularly regulation of oxidative stress and autophagy. Whether CUL3 is involved in mitigating NEFA-induced oxidative stress is unknown. This study aimed to investigate the protective effects and underlying mechanisms whereby CUL3 mitigates NEFA-induced oxidative stress in mammary epithelial cells. First, mammary gland tissue and blood samples were collected from healthy cows (n = 12, BHB <0.6 mM) and cows with clinical ketosis (n = 12, BHB >3.0 mM). Compared with healthy cows, cows with clinical ketosis had reduced productive performance, decreased CUL3 expression, impaired autophagic activity, and increased oxidative stress status in mammary tissue. In vitro, incubating the immortalized bovine mammary epithelial cell line (MAC-T) with 1.2 mM NEFA downregulated CUL3 expression, impaired autophagy, and increased oxidative stress. Adenovirus-mediated overexpression of CUL3 attenuated NEFA-induced accumulation of peroxides and reactive oxygen species, whereas silencing of CUL3 via small interfering RNA exacerbated these effects. Even when nuclear factor erythroid 2 related factor 2 (NFE2L2) expression was reduced by overexpression of CUL3, there was no worsening of NEFA-induced reductions in mRNA levels of NFE2L2 downstream target genes (NADPH quinone oxidoreductase 1 [NQO1], heme oxygenase-1 [HMOX1], glutamate-cysteine ligase catalytic subunit [GCLC)], and glutamate-cysteine ligase modifier subunit [GCLM]). The reduction in NEFA-induced oxidative stress by CUL3 was diminished upon autophagy related 5 (ATG5) silencing suggesting that CUL3 alleviates NEFA-induced oxidative stress via autophagy. Additionally, CUL3 overexpression aggravated the NEFA-induced decrease in BCL2 apoptosis regulator (BCL2) expression along with alleviating the NEFA-induced decrease in Beclin1 (BECN1) expression. Under NEFA treatment, overexpression of BCL2 partly mitigated the CUL3-induced elevation in BECN1. Overall, oxidative stress and impaired autophagy are characterized in the mammary tissue of cows with clinical ketosis. CUL3 activation, likely through the BCL2-BECN1 pathway, enhances autophagy and mitigates NEFA-induced oxidative stress in MAC-T cells. Thus, targeting CUL3-mediated autophagy could be a promising therapeutic strategy to reduce oxidative stress-induced damage in bovine mammary epithelial cells.

Animal health and nutrition: metabolic disorders in cattle and improvement strategies

The health and productivity of cattle are significantly compromised by metabolic diseases on a global scale. These disorders disrupt normal metabolic processes, leading to substantial economic losses for the livestock industry. Metabolic disorders can arise from defective biochemical pathways, deficiencies in enzymes, coenzymes, or cofactors, and may be either inherited or acquired. Dairy cows are particularly susceptible during the transition period from late lactation to early management, facing conditions such as ketosis, milk fever, and hepatic lipidosis. This susceptibility is primarily due to reduced dry matter intake caused by fetal development and a decline in rumen capacity. The negative energy balance (NEB) during this phase, characterized by elevated blood concentrations of non-esterified fatty acids (NEFAs) due to increased energy mobilization, is closely linked to the onset of these diseases. Providing high-energy-density diets during this period is critical to mitigating the effects of NEB. Metabolic disorders represent a major health challenge in cattle, adversely affecting animal welfare and agricultural output. A comprehensive understanding of their etiology, clinical manifestations, diagnostic approaches, and management strategies is essential for effective prevention and treatment. Ongoing research and the adoption of preventive measures are vital to reducing the economic and health impacts of these diseases. Early diagnosis and proactive management strategies are crucial to mitigating their impact on dairy cattle health and productivity. Early identification enables timely interventions, preventing disease progression and reducing adverse effects on animal health. Proactive measures, such as optimizing nutritional programs, implementing precision farming technologies, and ensuring timely veterinary care, are essential for enhancing the overall wellbeing of dairy cows. This review serves as a valuable resource for veterinarians, researchers, and dairy farmers, offering in-depth insights into the etiology, clinical signs, diagnostics, and management of prevalent metabolic disorders in dairy cattle. By equipping stakeholders with this knowledge, it aims to support informed decision-making and improve herd management practices. The focus on early diagnosis and proactive strategies underscores the potential to significantly reduce the economic and health burdens imposed by metabolic diseases on the livestock industry.

Transcriptomics reveals the regulatory mechanisms of circRNA in the muscle tissue of cows with ketosis postpartum

The transition period from late pregnancy to early lactation in dairy cows involves significant metabolic changes to cope with the challenges related to energy metabolism. Muscle tissue, as the largest energy-metabolizing tissue in dairy cows, plays a crucial role in energy metabolism. Furthermore, circular RNAs (circRNAs) have been shown to play key roles in various biological events. However, the regulatory mechanisms of energy metabolism and muscle cells mediated by circRNAs in the muscle tissue of ketotic dairy cows remain unclear. Here, we identified a total of 5103 circRNAs in the muscle tissue of ketosis-affected cows. Among these, compared to healthy cows, 57 circRNAs were differentially expressed in the muscle tissue of ketosis-affected cows, with 39 upregulated and 18 downregulated. Functional enrichment analysis based on the source genes of circRNAs indicated that circ-30,628 is closely related to carbon metabolism, and circ-CoQ2 is associated with mitochondrial energy metabolism. Given the sponge effect of circRNAs on miRNAs, we further predicted the network relationships of downstream miRNAs and mRNAs of circ-30,628 and circ-CoQ2, and found that their downstream target genes are involved in signaling pathways such as MAPK, Wnt, FoxO, and autophagy, which is associated with the proliferation, differentiation, energy metabolism, oxidative stress, and mitochondrial function of muscle cells. In summary, these findings provide a theoretical basis for understanding the functions of circRNAs regulating energy metabolism in the muscle tissue of ketosis-affected cows, thereby reducing the accumulation of ketone bodies to prevent the occurrence of ketosis in dairy cows.

Leveraging milk mid-infrared spectroscopy to authenticate animal welfare, farming practices, and dairy systems of Parmigiano Reggiano cheese

Increasing consumer concerns underscore the importance of verifying the practices and origins of food, especially certified premium products. The aim of this study was to evaluate the ability of Fourier-transform mid-infrared (FT-MIR) spectroscopy to authenticate animal welfare parameters, farming practices, and dairy systems. Data on farm characteristics were obtained from the Parmigiano Reggiano Consortium in northern Italy. Animal welfare data were collected by trained veterinarians using the assessment protocol developed by the Italian National Reference Center for Animal Welfare (CReNBA), and bulk milk test-day data were obtained from the laboratory of the Breeders Association of the Emilia Romagna Region. A merged final dataset of 12,083 bulk FT-MIR spectra records from 949 farms was created. Using a nonhierarchical clustering approach, the farms were classified into 5 dairy systems: 2 traditional systems comprising farms located in either the Apennines or the Po Plain; 2 modern systems, one that used TMR and one did not; and one traditional dairy system comprising farms rearing local breeds. To evaluate the ability of bulk milk to capture differences in farming systems, we conducted an ANOVA on milk composition. The linear models included the following effects: season, dairy system, farm, and the interaction between dairy system and season. The effect of the dairy system was significant for all milk composition traits. A 10-iteration linear discriminant analysis was used to evaluate the discriminative ability of the spectra in classifying farming practices and dairy systems. The average results of the area under the receiver operating characteristic curve revealed good authentication performance for genetic type (0.98), housing system (0.91), and feeding system (0.89), and medium-low authentication performance for geographical area (0.70); poor results were obtained for the percentage of concentrate in the diet and animal welfare parameters (0.57-0.64). With regard to dairy systems, the best result was obtained when dairy systems were grouped into 2 simplified categories, traditional versus modern (0.89), instead of the 5 categories (0.87). The results of this study show that FT-MIR is a useful tool for authenticating farming practices and dairy systems, but not animal welfare as defined by CReNBA evaluation criteria. Our results show that infrared spectroscopy has the potential to authenticate dairy products and quality label certifications.

Prediction of ketosis using radial basis function neural network in dairy cattle farming

The purpose of the paper was to apply an Artificial Neural Networks with Radial Basis Function to develop an application model for diagnosing a subclinical ketosis type I and II in dairy cattle. While building the neural network model, applied methodology was compatible to the procedures used in Data Mining processes. The data set was created based on the composition of milk samples of 1520 Polish Holstein-Friesian cows. The milk samples were collected during test-day milkings and made available by Polish Federation of Cattle Breeders and Milk Producers. The milk composition parameters were used as the input variables for RBF network models. The value of the output variable was determined based on the content of β-hydroxybutyric acid in blood of cows. In the next stage of the work, the qualities of the pre-selected models were compared and the best ones were chosen. The sensitivity and specificity as well as the size of the AUC (Area Under the Curve) under the ROC (Receiver Operating Characteristic) were taken as the main criteria for network models evaluation. The model characterized by sensitivity of 0.86, specificity of 0.71 and AUC of 0.89 was selected for ketosis type I. The optimal for ketosis type II showed the sensitivity and specificity 0.81 and 0.75, respectively, and the size of AUC above 0.85. Chosen models were recorded using the predictive modelling markup language (PMML) for data mining models to be shared and used between the different applications.

Functionalized Graphene-Based Biosensors for Early Detection of Subclinical Ketosis in Dairy Cows

Precision livestock farming utilizing advanced diagnostic tools, including biosensors, can play a key role in the management of livestock operations to improve the productivity, health, and well-being of animals. Detection of ketosis, a metabolic disease that occurs in early lactation dairy cows due to a negative energy balance, is one potential on-farm use of biosensors. Beta-hydroxybutyrate (βHB) is an excellent biomarker for monitoring ketosis in dairy cows because βHB is one of the main ketones produced during this metabolic state. In this report, we developed a low-cost, Keto-sensor (graphene-based sensor) for the detection of βHB concentrations in less than a minute. On this device, graphene nanosheets were layered onto a screen-printed electrode (SPE), and then, a stabilized enzyme (beta-hydroxybutyrate dehydrogenase, NAD+, and glycerol) was used to functionalize the graphene surface enabled by EDC-NHS conjugation chemistry. The Keto-sensor offers an analytical sensitivity of 10 nm and a limit of detection (LoD) of 0.24 nm within a detection range of 0.01 μm-3.00 mm. Spike testing indicates that the Keto-sensor can detect βHB in serum samples from bovines with subclinical ketosis. The Keto-sensor developed in this study shows promising results for early detection of subclinical ketosis on farms.

Diabetes and aortic dissection: unraveling the role of 3-hydroxybutyrate through mendelian randomization

BACKGROUND

In observational and experimental studies, diabetes has been reported as a protective factor for aortic dissection. 3-Hydroxybutyrate, a key constituent of ketone bodies, has been found to favor improvements in cardiovascular disease. However, whether the protective effect of diabetes on aortic dissection is mediated by 3-hydroxybutyrate is unclear. We aimed to investigate the causal effects of diabetes on the risk of aortic dissection and the mediating role of 3-hydroxybutyrate in them through two-step Mendelian randomization.

MATERIALS AND METHODS

We performed a two-step Mendelian randomization to investigate the causal connections between diabetes, 3-hydroxybutyrate, and aortic dissection and calculate the mediating effect of 3-hydroxybutyrate. Publicly accessible data for Type 1 diabetes, Type 2 diabetes, dissection of aorta and 3-hydroxybutyrate were obtained from genome-wide association studies. The association between Type 1 diabetes and dissection of aorta, the association between Type 2 diabetes and dissection of aorta, and mediation effect of 3-hydroxybutyrate were carried out separately.

RESULTS

The IVW method showed that Type 1 diabetes was negatively associated with the risk of aortic dissection (OR 0.912, 95% CI 0.836-0.995), The weighted median, simple mode and weighted mode method showed consistent results. The mediated proportion of 3-hydroxybutyrate on the relationship between Type 1 diabetes and dissection of aorta was 24.80% (95% CI 5.12-44.47%). The IVW method showed that Type 2 diabetes was negatively associated with the risk of aortic dissection (OR 0.763, 95% CI 0.607-0.960), The weighted median, simple mode and weighted mode method showed consistent results. 3-Hydroxybutyrate does not have causal mediation effect on the relationship between Type 2 diabetes and dissection of aorta.

CONCLUSION

Mendelian randomization study revealed diabetes as a protective factor for dissection of aorta. The protective effect of type 1 diabetes on aortic dissection was partially mediated by 3-hydroxybutyrate, but type 2 diabetes was not 3-hydroxybutyrate mediated.

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.

Multi-omics analyses reveal rumen microbes and secondary metabolites that are unique to livestock species

Ruminant livestock, including cattle, sheep, goats, and camels, possess a distinctive digestive system with complex microbiota communities critical for feed conversion and secondary metabolite production, including greenhouse gases. Yet, there is limited knowledge regarding the diversity of rumen microbes and metabolites benefiting livestock physiology, productivity, climate impact, and defense mechanisms across ruminant species. In this study, we utilized metataxonomics and metabolomics data from four evolutionarily distinct livestock species, which had fed on diverse plant materials like grass, shrubs, and acacia trees, to uncover the unique signature microbes and secondary metabolites. We established the presence of a distinctive anaerobic fungus called Oontomyces in camels, while cattle exhibited a higher prevalence of unique microbes like Psychrobacter, Anaeromyces, Cyllamyces, and Orpinomyces. Goats hosted Cleistothelebolus, and Liebetanzomyces was unique to sheep. Furthermore, we identified a set of conserved core microbes, including Prevotella, Rickenellaceae, Cladosporium, and Pecoramyces, present in all the ruminants, irrespective of host genetics and dietary composition. This underscores their indispensable role in maintaining crucial physiological functions. Regarding secondary metabolites, camel's rumen is rich in organic acids, goat's rumen is rich in alcohols and hydrocarbons, sheep's rumen is rich in indoles, and cattle's rumen is rich in sesquiterpenes. Additionally, linalool propionate and terpinolene were uniquely found in sheep rumen, while valencene was exclusive to cattle. This may suggest the existence of species-specific microbes and metabolites that require host rumen-microbes' environment balance. These results have implications for manipulating the rumen environment to target specific microbes and secondary metabolite networks, thereby enhancing livestock productivity, resilience, reducing susceptibility to vectors, and environmentally preferred livestock husbandry.IMPORTANCERumen fermentation, which depends on feed components and rumen microbes, plays a crucial role in feed conversion and the production of various metabolites important for the physiological functions, health, and environmental smartness of ruminant livestock, in addition to providing food for humans. However, given the complexity and variation of the rumen ecosystem and feed of these various livestock species, combined with inter-individual differences between gut microbial communities, how they influence the rumen secondary metabolites remains elusive. Using metagenomics and metabolomics approaches, we show that each livestock species has a signature microbe(s) and secondary metabolites. These findings may contribute toward understanding the rumen ecosystem, microbiome and metabolite networks, which may provide a gateway to manipulating rumen ecosystem pathways toward making livestock production efficient, sustainable, and environmentally friendly.

Structural equation models to infer relationships between energy-related blood metabolites and milk daily energy output in Holstein cows

During lactation, high-yielding cows experience metabolic disturbances due to milk production. Metabolic monitoring offers valuable insights into how cows manage these challenges throughout the lactation period, making it a topic of considerable interest to breeders. In this study, we used Bayesian networks to uncover potential dependencies among various energy-related blood metabolites, i.e., glucose, urea, beta-hydroxybutyrate (BHB), non-esterified fatty acids (NEFA), cholesterol (CHOL), and daily milk energy output (dMEO) in 1,254 Holstein cows. The inferred causal structure was then incorporated into structural equation models (SEM) to estimate heritabilities and additive genetic correlations among these phenotypes using both pedigree and genotypes from a 100k chip. Dependencies among traits were determined using the Hill-Climbing algorithm, implemented with the posterior distribution of the residuals obtained from the standard multiple-trait model. These identified relationships were then used to construct the SEM, considering both direct and indirect relationships. The relevant dependencies and path coefficients obtained, expressed in units of measurement variation of 1σ, were as follows: dMEO → CHOL (0.181), dMEO → BHB (-0.149), dMEO → urea (0.038), glucose → BHB (-0.55), glucose → urea (-0.194), CHOL → urea (0.175), BHB → urea (-0.049), and NEFA → urea (-0.097). Heritabilities for traits of concern obtained with SEM ranged from 0.09 to 0.2. Genetic correlations with a minimum 95% probability (P) of the posterior mean being >0 for positive means or <0 for negative means include those between dMEO and glucose (-0.583, P = 100), dMEO and BHB (0.349, P = 99), glucose and CHOL (0.325, P = 100), glucose and NEFA (-0.388, P = 100), and NEFA and BHB (0.759, P = 100). The results of this analysis revealed the existence of recursive relationships among the energy-related blood metabolites and dMEO. Understanding these connections is paramount for establishing effective genetic selection strategies, enhancing production and animal welfare.

Gene association analysis of an osteopontin polymorphism and ketosis resistance in dairy cattle

The aim of this study was to identify the c.495C > T polymorphism within exon 1 of the osteopontin gene (OPN), and to analyze its association with susceptibility to ketosis in Polish Holstein-Friesian (HF) cows. The study utilized blood samples from 977 HF cows, for the determination of β-hydroxybutyric acid (BHB) and for DNA isolation. The c.495C > T polymorphism of the bovine osteopontin gene was determined by PCR-RFLP. The CT genotype (0.50) was deemed the most common, while TT (0.08) was the rarest genotype. Cows with ketosis most often had the CC genotype, while cows with the TT genotype had the lowest incidence of ketosis. To confirm the relationship between the genotype and ketosis in cows, a weight of evidence (WoE) was generated. A very strong effect of the TT genotype on resistance to ketosis was demonstrated. The distribution of the ROC curve shows that the probability of resistance to ketosis is > 75% if cows have the TT genotype of the OPN gene (cutoff value is 0.758). Results suggest that TT genotype at the c.495C > T locus of the OPN gene might be effective way to detect the cows with risk of ketosis.

Lipolysis inhibition as a treatment of clinical ketosis in dairy cows: A randomized clinical trial

Excessive and protracted lipolysis in adipose tissues of dairy cows is a major risk factor for clinical ketosis (CK). This metabolic disease is common in postpartum cows when lipolysis provides fatty acids as an energy substrate to offset negative energy balance. Lipolysis in cows can be induced by the canonical (hormonally induced) and inflammatory pathways. Current treatments for CK focus on improving glucose in blood (i.e., oral propylene glycol [PG], or i.v. dextrose). However, these therapies do not inhibit the canonical and inflammatory lipolytic pathways. Niacin (NIA) can reduce activation of the canonical pathway. Blocking inflammatory responses with cyclooxygenase inhibitors such as flunixin meglumine (FM) can inhibit inflammatory lipolytic activity. The objective of this study was to determine the effects of including NIA and FM in the standard PG treatment for postpartum CK on circulating concentrations of ketone bodies. A 4-group, parallel, individually randomized trial was conducted in multiparous Jersey cows (n = 80) from a commercial dairy in Michigan during a 7-mo period. Eligible cows had CK symptoms (lethargy, depressed appetite, and milk yield) and hyperketonemia (blood β-hydroxybutyrate [BHB] ≥1.2 mmol/L). Cows with CK were randomly assigned to 1 of 3 groups where the first group received 310 g of oral PG once per day for 5 d; the second group received PG for 5 d + 24 g of oral NIA once per day for 3 d (PGNIA); and the third group received PG for 5 d + NIA for 3 d + 1.1 mg/kg i.v. FM once per day for 3 d (PGNIAFM). The control group consisted of cows that were clinically healthy (HC; untreated; BHB <1.2 mmol/L, n = 27) matching for parity and DIM with all 3 groups. Animals were sampled at enrollment (d 0), and d 3, 7, and 14 to evaluate ketone bodies and circulating metabolic and inflammatory biomarkers. Effects of treatment, sampling day, and their interactions were evaluated using mixed effects models. Logistic regression was used to calculate the odds ratio (OR) of returning to normoketonemia (BHB <1.2 mmol/L). Compared with HC, enrolled CK cows exhibited higher blood concentrations of dyslipidemia markers, including nonesterified fatty acids (NEFA) and BHB, and lower glucose and insulin levels. Cows with CK also had increased levels of biomarkers of pain (substance P), inflammation, including lipopolysaccharide-binding protein, haptoglobin, and serum amyloid A, and proinflammatory cytokines IL-4, MCP-1, MIP-1α, and TNFα. Importantly, 72.2% of CK cows presented endotoxemia and had higher circulating bacterial DNA compared with HC. By d 7, the percentage of cows with normoketonemia were higher in PGNIAFM = 87.5%, compared with PG = 58.33%, and PGNIA = 62.5%. At d 7 the OR for normoketonemia in PGNIAFM cows were 1.5 (95% CI, 1.03-2.17) and 1.4 (95% CI, 0.99-1.97) relative to PG and PGNIA, respectively. At d 3, 7, and 14, PGNIAFM cows presented the lowest values of BHB (PG = 1.36; PGNIA = 1.24; PGNIAFM = 0.89 ± 0.13 mmol/L), NEFA (PG = 0.58; PGNIA = 0.59; PGNIAFM = 0.45 ± 0.02 mmol/L), and acute phase proteins. Cows in PGNIAFM also presented the highest blood glucose increment across time points and insulin by d 7. These data provide evidence that bacteremia or endotoxemia, systemic inflammation, and pain may play a crucial role in CK pathogenesis. Additionally, targeting lipolysis and inflammation with NIA and FM during CK effectively reduces dyslipidemia biomarkers, improves glycemia, and improves overall clinical recovery.

β-Hydroxybutyrate Effects on Bovine Caruncular Epithelial Cells: A Model for Investigating the Peri-Implantation Period Disruption in Ketotic Dairy Cows

Ketosis is a metabolic disorder arising from a negative energy balance (NEB). It is characterized by high β-Hydroxybutyrate (BHBA) blood levels and associated with reduced fertility in dairy cows. To investigate the impact of BHBA on bovine caruncular epithelial cells (BCEC) in vitro, these cells were stimulated with different concentrations of BHBA. Cell metabolism and motility were examined using an MTT assay and Live-cell imaging. RT-qPCR was used to examine mRNA expressions of TNF, IL6, RELA, prostaglandin E2 synthase (PTGES2) and receptor (PTGER2) as well as integrin subunits ITGAV, ITGA6, ITGB1 and ITGB3. Stimulation with 1.8 and 2.4 mM of BHBA negatively affected cell metabolism and motility. TNF showed increased mRNA expression related to rising BHBA concentrations. IL6, RELA, ITGAV, ITGA6, ITGB1 and ITGB3 as well as PTGER2 showed no changes in mRNA expression. Stimulation with 0.6 and 1.2 mM of BHBA significantly increased the mRNA expression of PTGES2. This does not indicate a negative effect on reproductive performance because low BHBA concentrations are found in steady-state conditions. However, the results of the study show negative effects of high BHBA concentrations on the function of BCECs as well as an inflammatory response. This could negatively affect the feto-maternal communication during the peri-implantation period in ketotic dairy cows.

Characteristics of Holstein cows predisposed to ketosis during the post-partum transition period

BACKGROUND

Ketosis is a common metabolic disorder during the post-partum transition period of dairy cattle. How the method of reproduction, parturition time, and calf birth weight affect the occurrence of ketosis on dairy herds remains elusive.

OBJECTIVES

This study investigated factors associated with the severity of ketosis.

METHODS

We divided 186 Holstein cows into three classifications based on the highest β-hydroxybutyrate (BHBA) concentration during the post-partum transition period, namely non-ketosis (<1.2 mmol/L, n = 94), subclinical ketosis (1.2-2.9 mmol/L, n = 58), and clinical ketosis (≥3.0 mmol/L, n = 34). We evaluated characteristics of cows associated with the severity of ketosis.

RESULTS

Ketosis was not associated with the method of reproduction, parturition time, pregnancy wastage, premature delivery, retained placenta, and type of calf. Cows calving in spring and especially summer were at higher risk of severe ketosis (p < 0.01). Cows with increased body condition score (BCS) at parturition, age, lactation number, and calving interval were more likely to develop severe ketosis (p < 0.05). Cows with clinical ketosis produced most milk (29.9 ± 1.0 kg) from days four to six, whereas cows without ketosis produced the least (21.3 ± 0.8 kg) (p < 0.001). Heavier calf birth weight resulted in high risk of severe ketosis (p < 0.01), due to increased milk yield during the early lactation.

CONCLUSIONS

The severity of ketosis is associated with the calving season, BCS at parturition, age, lactation number, calving interval, milk yield in the early lactation period, and calf birth weight. Nonetheless, it was not associated with the method of reproduction, parturition time, pregnancy wastage, premature delivery, retained placenta, and type of calf. This study is the first to investigate the associations between ketosis and calf birth weight. Our findings could help predict cows at risk of ketosis and take precautions.

Effect of Breed on the Fatty Acid Composition of Milk from Dairy Cows Milked Once and Twice a Day in Different Stages of Lactation

The objective of this study was to evaluate the effect of breed on the overall composition and fatty acid composition of milk from cows milked once a day (OAD) and twice a day (TAD) in different stages of lactation. Milk samples were taken from 39 Holstein-Friesian (F), 27 Jersey (J), and 34 Holstein-Friesian × Jersey (F × J) crossbred cows from a OAD milking herd and 104 F and 83 F × J cows from a TAD milking herd in early (49 ± 15 days in milk), mid (129 ± 12 days in milk), and late (229 ± 13 days in milk) lactation. Calibration equations to predict the concentrations of individual fatty acids were developed using mid-infrared (MIR) spectroscopy. There was a significant interaction between breed within the milking frequency and stage of lactation for the production traits and composition traits. Holstein-Friesian cows milked OAD produced milk with lower concentrations of C18:0 in early and mid lactations compared to F × J and J cows. Holstein-Friesian cows milked TAD produced lower concentrations of C18:0 in early lactation and lower concentrations of C16:0 and C18:0 in late lactation compared to F × J. Lower concentrations of these fatty acids would reduce the hardness of the butter when the milk is processed. In the OAD milking herd, F cows were superior for daily milk yield compared to J cows, but Jersey cows produced significantly (p < 0.05) higher percentages of fat and a higher concentration of C18:0 fatty acid. The relative concentrations of C18:0 and C18 cis-9 in F and J cows milked OAD imply there is no breed effect on the activity of delta-9-desaturase, whereas stages of lactation likely have an effect. These results can be used to assist with selecting breeds and cows that are suitable for either OAD or TAD milking, allowing closer alignment with milk processing needs.

The Use of Multilayer Perceptron Artificial Neural Networks to Detect Dairy Cows at Risk of Ketosis

Simple Summary

Ketosis is a serious metabolic disease in high-yield dairy cows, that affects productive herds throughout the world. Subclinical ketosis is one of the most dominant metabolic disorders in dairy herds during early lactation, so early detection and prevention are important for both economic and animal welfare reasons. Neural networks, which offer a high degree of accuracy in predicting various phenomena and processes where there is no clear causal correlation or there are no rules that allow the establishment of a logical cause-and-effect relationship, can be used to address problems related to prediction, classification, or control. A Multi-Layer perceptron (MLP) is a feedforward artificial neural network model that takes input data for a set of proper output. This study investigated the performance of four algorithms used to train MLP networks. The experimental results demonstrate that the MLP network model improved the accuracy of process recognition of subclinical ketosis in dairy cows. The received artificial model’s results were saved in the predictive model markup language (PMML) and can be used to describe the learning set, the algorithm used in the data mining application and related information.

Abstract

Subclinical ketosis is one of the most dominant metabolic disorders in dairy herds during lactation. Cows suffering from ketosis experience elevated ketone body levels in blood and milk, including β-hydroxybutyric acid (BHB), acetone (ACE) and acetoacetic acid. Ketosis causes serious financial losses to dairy cattle breeders and milk producers due to the costs of diagnosis and management as well as animal welfare reasons. Recent years have seen a growing interest in the use of artificial neural networks (ANNs) in various fields of science. ANNs offer a modeling method that enables the mapping of highly complex functional relationships. The purpose of this study was to determine the relationship between milk composition and blood BHB levels associated with subclinical ketosis in dairy cows, using feedforward multilayer perceptron (MLP) artificial neural networks. The results were verified based on the estimated sensitivity and specificity of selected network models, an optimum cut-off point was identified for the receiver operating characteristic (ROC) curve and the area under the ROC curve (AUC). The study demonstrated that BHB, ACE and lactose (LAC) levels, as well as the fat-to-protein ratio in milk, were important input variables in the network training process. For the identification of cows at risk of subclinical ketosis, variables such as BHB and ACE levels in milk were of particular relevance, with a sensitivity and specificity of 0.84 and 0.61, respectively. It was found that the back propagation algorithm offers opportunities to integrate artificial intelligence and dairy cattle welfare within a computerized decision support tool.