Milk metabolites and fatty acids as noninvasive biomarkers of metabolic status and energy balance in early-lactation cows

Milk components as potential indicators of energy status in early lactation Holstein dairy cows from two farms

Negative energy balance (NEB) is a serious problem in most dairy cows. It occurs most frequently after calving, when cows are unable to consume sufficient DM to meet their energy requirements during early lactation. During NEB, the breakdown of fat stores releases non-esterified fatty acids (NEFAs) into the bloodstream. High blood concentrations of NEFAs cause health problems such as ketosis, fatty liver syndrome, and enhanced susceptibility to infections. These issues may substantially increase premature culling from the herd. Serum NEFA concentrations are often used as a direct marker of energy metabolism. However, because the direct measurement of serum NEFAs is difficult under commercial conditions, alternative indicators, such as milk components, have been increasingly investigated for their use in estimating energy balance. The objectives of this study were to (1) evaluate the relationships between serum NEFA concentrations and selected milk components in cows from two farms during the first 5 weeks of lactation, and to (2) develop a model valid for both herds for predicting serum NEFA concentrations using milk components. A total of 121 lactating Holstein cows from two different farms were included in the experiment. Blood samples were collected for NEFA analysis on days 7 (± 3), 14 (± 3), 21 (± 3), and 35 (± 3) after calving. Composite milk samples were collected during afternoon milking on the same days as blood sampling. Concentrations of fat, protein, lactose, and milk fatty acids (FAs) were determined using Fourier-transform IR spectroscopy analysis. The strongest correlations (r > 0.43) were recorded between serum NEFAs and milk long-chain FAs, monounsaturated FAs, C18:0, and C18:1 within each farm and for both farms combined. Two prediction models for serum log(NEFA) using milk components as predictors were developed by stepwise regression. The prediction model with the best fit (R2 = 0.52) included days in milk, fat-to-protein ratio, and C18:1, C18:12 and C14:0 expressed as g/100 g of milk fat. An essential finding is that, despite different concentrations of NEFAs, and of most milk components observed in the evaluated herds, there were no significant interactions between farm and any of the FAs, so the same regression coefficients could be used for the prediction models in both farms. Validation of these findings in a greater number of herds would allow for the use of milk FAs to identify energy-imbalanced cows in herds under different farm conditions.

Assessment of milk metabolites as biomarkers for predicting feed efficiency in dairy sheep

Estimating feed efficiency (FE) in dairy sheep is challenging due to the high cost of systems that measure individual feed intake. Identifying proxies that can serve as effective predictors of FE could make it possible to introduce FE into breeding programs. Here, 39 Assaf ewes in first lactation were evaluated regarding their FE by 2 metrics, residual feed intake (RFI) and feed conversion ratio (FCR). The ewes were classified into high, medium and low groups for each metric. Milk samples of the 39 ewes were subjected to untargeted metabolomics analysis. The complete milk metabolomic signature was used to discriminate the FE groups using partial least squares discriminant analysis. A total of 41 and 26 features were selected as the most relevant features for the discrimination of RFI and FCR groups, respectively. The predictive ability when utilizing the complete milk metabolomic signature and the reduced data sets were investigated using 4 machine learning (ML) algorithms and a multivariate regression method. The orthogonal partial least squares algorithm outperformed other ML algorithms for FCR prediction in the scenarios using the complete milk metabolite signature (R2 = 0.62 ± 0.06) and the 26 selected features (R2 = 0.62 ± 0.15). Regarding RFI predictions, the scenarios using the 41 selected features outperformed the scenario with the complete milk metabolite signature, where the multilayer feedforward artificial neural network (R2 = 0.18 ± 0.14) and extreme gradient boosting (R2 = 0.17 ± 0.15) outperformed other algorithms. The functionality of the selected metabolites implied that the metabolism of glucose, galactose, fructose, sphingolipids, amino acids, insulin, and thyroid hormones was at play. Compared with the use of traditional methods, practical applications of these biomarkers might simplify and reduce costs in selecting feed-efficient ewes.

Multiomics analysis revealed that the metabolite profile of raw milk is associated with lactation stage of dairy cows and could be affected by variations in the ruminal microbiota

The nutritional components and quality of milk are influenced by the rumen microbiota and its metabolites at different lactation stages. Hence, rumen fluid and milk samples from 6 dairy cows fed the same diet were collected during peak, early mid- and later mid-lactation. Untargeted metabolomics and 16S rRNA sequencing were applied for analyzing milk and rumen metabolites, as well as rumen microbial composition, respectively. The levels of lipid-related metabolites, L-glutamate, glucose-1-phosphate and acetylphosphate in milk exhibited lactation-dependent attenuation. Maltol, N-acetyl-D-glucosamine, and choline, which are associated with milk flavor or coagulation properties, as well as L-valine, lansioside-A, clitocine and ginsenoside-La increased significantly in early mid- and later mid-lactation, especially in later mid-lactation. The obvious increase in rumen microbial diversities (Ace and Shannon indices) were observed in early mid-lactation compared with peak lactation. Twenty-one differential bacterial genera of the rumen were identified, with Succinivibrionaceae_UCG-001, Candidatus Saccharimonas, Fibrobacter, and SP3-e08 being significantly enriched in peak lactation. Rikenellaceae_RC9_gut_group, Eubacterium_ruminantium_group, Lachnospira, Butyrivibrio, Eubacterium_hallii_group, and Schwartzia were most significantly enriched in early mid-lactation. In comparison, only 2 bacteria (unclassified_f__Prevotellaceae and Prevotellaceae_UCG-001) were enriched in later mid-lactation. For rumen metabolites, LPE(16:0), L-glutamate and L-tyrosine had higher levels in peak lactation, whereas PE(17:0/0:0), PE(16:0/0:0), PS(18:1(9Z)/0:0), L-phenylalanine, dulcitol, 2-(methoxymethyl)furan and 3-phenylpropyl acetate showed higher levels in early mid- and later mid-lactation. Multiomics integrated analysis revealed that a greater abundance of Fibrobacter contributed to phospholipid content in milk by increasing ruminal acetate, L-glutamate and LysoPE(16:0). Prevotellaceae_UCG-001 and unclassified_f_Prevotellaceae provide substrates for milk metabolites of the same category by increasing ruminal L-phenylalanine and dulcitol contents. These results demonstrated that milk metabolomic fingerprints and critical functional metabolites during lactation, and the key bacteria in rumen related to them. These findings provide new insights into the development of functional dairy products.

Milk Odd- and Branched-Chain Fatty Acids as Biomarkers of Rumen Fermentation

Cow's milk and dairy products are the primary sources of OBCFAs, which have beneficial health properties. The goal of this study was to identify the factors that influence the content of OBCFAs in cow's milk and to indicate which OBCFAs can serve as biomarkers for fermentation processes. The content of OBCFAs in milk depends on the species of ruminants, with studies showing that this varies between 3.33% (in goat's milk) and 5.02% (in buffalo's milk). These differences also stem from the animals' energy balance, lactation phases, forage-to-concentrate ratio, and the presence of bioactive compounds in feeds, as well as management practices and environmental conditions. The OBCFAs in milk fat mainly come from rumen bacteria, but can also be synthesized de novo in the mammary gland, making them potentially useful noninvasive indicators of rumen fermentation. The concentration of BCFA is lower in colostrum and transitional milk than in full lactation milk. The proportions of total OBCFAs are higher in first- and second-parity cows. The most effective predictors of the biohydrogenation of fatty acids in the rumen are likely C18:2 cis-9, trans-11, iso-C16:0, and iso-C13:0. OBCFAs have been identified as potential biomarkers for rumen function, because their synthesis depends on specific bacteria. Strong predictors of subclinical ruminal acidosis include iso-C14:0, iso-C13:0, and C15:0. The concentration of ∑ OBCFA >C16 in milk is associated with fat mobilization and serves as a significant marker of the energy balance in cows.

Genetic analysis of milk citrate predicted by milk mid-infrared spectra of Holstein cows in early lactation

Milk citrate is regarded as an early biomarker of negative energy balance in dairy cows during early lactation and serves as a suitable candidate phenotype for genomic selection due to its wide availability across a large number of cows through milk mid-infrared spectra prediction. However, its genetic background is not well known. Therefore, the objectives of this study were to (1) analyze the genetic parameters of milk citrate; (2) identify genomic regions associated with milk citrate; and (3) analyze the functional annotation of candidate genes and quantitative trait loci (QTL) related to milk citrate in Walloon Holstein cows. In total, 134,517 test-day milk-citrate phenotypes (mmol/L) collected within the first 50 d in milk on 52,198 Holstein cows were used. These milk-citrate phenotypes, predicted by milk mid-infrared spectra, were divided into 3 traits according to the first (citrate1), second (citrate2), and third to fifth parity (citrate3+). Genomic information for 566,170 SNPs was available for 4,479 animals. A multiple-trait repeatability model was used to estimate genetic parameters. A single-step GWAS was used to identify candidate genes for citrate and post-GWAS analysis was done to investigate the relationship and function of the identified candidate genes. The heritabilities estimated for citrate1, citrate2, and citrate3+ were 0.40, 0.37, and 0.35, respectively. The genetic correlations among the 3 traits ranged from 0.98 to 0.99. The genomic correlations among the 3 traits were also close to 1.00 across the genomic regions (1 Mb) in the whole genome, which means that citrate can be considered as a single trait in the first 5 parities. In total, 603 significant SNPs located on 3 genomic regions (chromosome 7, 68.569-68.575 Mb; chromosome 14, 0.15-1.90 Mb; and chromosome 20, 54.00-64.28 Mb), were identified to be associated with milk citrate. We identified 89 candidate genes including GPT, ANKH, PPP1R16A, and 32 QTL reported in the literature related to the identified significant SNPs. These identified QTL were mainly reported associated with milk fatty acids and metabolic diseases in dairy cows. This study suggests that milk citrate in Holstein cows is highly heritable and has the potential to be used as an early proxy for the negative energy balance of Holstein cows in a breeding objective.

Discrimination of Lipogenic or Glucogenic Diet Effects in Early-Lactation Dairy Cows Using Plasma Metabolite Abundances and Ratios in Combination with Machine Learning

During early lactation, dairy cows have a negative energy balance since their energy demands exceed their energy intake: in this study, we aimed to investigate the association between diet and plasma metabolomics profiles and how these relate to energy unbalance of course in the early-lactation stage. Holstein-Friesian cows were randomly assigned to a glucogenic (n = 15) or lipogenic (n = 15) diet in early lactation. Blood was collected in week 2 and week 4 after calving. Plasma metabolite profiles were detected using liquid chromatography-mass spectrometry (LC-MS), and a total of 39 metabolites were identified. Two plasma metabolomic profiles were available every week for each cow. Metabolite abundance and metabolite ratios were used for the analysis using the XGboost algorithm to discriminate between diet treatment and lactation week. Using metabolite ratios resulted in better discrimination performance compared with the metabolite abundances in assigning cows to a lipogenic diet or a glucogenic diet. The quality of the discrimination of performance of lipogenic diet and glucogenic diet effects improved from 0.606 to 0.753 and from 0.696 to 0.842 in week 2 and week 4 (as measured by area under the curve, AUC), when the metabolite abundance ratios were used instead of abundances. The top discriminating ratios for diet were the ratio of arginine to tyrosine and the ratio of aspartic acid to valine in week 2 and week 4, respectively. For cows fed the lipogenic diet, choline and the ratio of creatinine to tryptophan were top features to discriminate cows in week 2 vs. week 4. For cows fed the glucogenic diet, methionine and the ratio of 4-hydroxyproline to choline were top features to discriminate dietary effects in week 2 or week 4. This study shows the added value of using metabolite abundance ratios to discriminate between lipogenic and glucogenic diet and lactation weeks in early-lactation cows when using metabolomics data. The application of this research will help to accurately regulate the nutrition of lactating dairy cows and promote sustainable agricultural development.

Prediction of key milk biomarkers in dairy cows through milk mid-infrared spectra and international collaborations

At the individual cow level, suboptimum fertility, mastitis, negative energy balance, and ketosis are major issues in dairy farming. These problems are widespread on dairy farms and have an important economic impact. The objectives of this study were (1) to assess the potential of milk mid-infrared (MIR) spectra to predict key biomarkers of energy deficit (citrate, isocitrate, glucose-6 phosphate [glucose-6P], free glucose), ketosis (β-hydroxybutyrate [BHB] and acetone), mastitis (N-acetyl-β-d-glucosaminidase activity [NAGase] and lactate dehydrogenase), and fertility (progesterone); (2) to test alternative methodologies to partial least squares (PLS) regression to better account for the specific asymmetric distribution of the biomarkers; and (3) to create robust models by merging large datasets from 5 international or national projects. Benefiting from this international collaboration, the dataset comprised a total of 9,143 milk samples from 3,758 cows located in 589 herds across 10 countries and represented 7 breeds. The samples were analyzed by reference chemistry for biomarker contents, whereas the MIR analyses were performed on 30 instruments from different models and brands, with spectra harmonized into a common format. Four quantitative methodologies were evaluated to address the strongly skewed distribution of some biomarkers. Partial least squares regression was used as the reference basis, and compared with a random modification of distribution associated with PLS (random-downsampling-PLS), an optimized modification of distribution associated with PLS (KennardStone-downsampling-PLS), and support vector machine (SVM). When the ability of MIR to predict biomarkers was too low for quantification, different qualitative methodologies were tested to discriminate low versus high values of biomarkers. For each biomarker, 20% of the herds were randomly removed within all countries to be used as the validation dataset. The remaining 80% of herds were used as the calibration dataset. In calibration, the 3 alternative methodologies outperform the PLS performances for the majority of biomarkers. However, in the external herd validation, PLS provided the best results for isocitrate, glucose-6P, free glucose, and lactate dehydrogenase (coefficient of determination in external herd validation [R2v] = 0.48, 0.58, 0.28, and 0.24, respectively). For other molecules, PLS-random-downsampling and PLS-KennardStone-downsampling outperformed PLS in the majority of cases, but the best results were provided by SVM for citrate, BHB, acetone, NAGase, and progesterone (R2v = 0.94, 0.58, 0.76, 0.68, and 0.15, respectively). Hence, PLS and SVM based on the entire dataset provided the best results for normal and skewed distributions, respectively. Complementary to the quantitative methods, the qualitative discriminant models enabled the discrimination of high and low values for BHB, acetone, and NAGase with a global accuracy around 90%, and glucose-6P with an accuracy of 83%. In conclusion, MIR spectra of milk can enable quantitative screening of citrate as a biomarker of energy deficit and discrimination of low and high values of BHB, acetone, and NAGase, as biomarkers of ketosis and mastitis. Finally, progesterone could not be predicted with sufficient accuracy from milk MIR spectra to be further considered. Consequently, MIR spectrometry can bring valuable information regarding the occurrence of energy deficit, ketosis, and mastitis in dairy cows, which in turn have major influences on their fertility and survival.

Combination of milk variables and on-farm data as an improved diagnostic tool for metabolic status evaluation in dairy cattle during the transition period

Milk composition, particularly milk fatty acids, has been extensively studied as an indicator of the metabolic status of dairy cows during early lactation. In addition to milk biomarkers, on-farm sensor data also hold potential in providing insights into the metabolic health status of cows. While numerous studies have explored the collection of a wide range of sensor data from cows, the combination of milk biomarkers and on-farm sensor data remains relatively underexplored. Therefore, this study aims to identify associations between metabolic blood variables, milk variables, and various on-farm sensor data. Second, it seeks to examine the supplementary or substitutive potential of these data sources. Therefore, data from 85 lactations on metabolic status and on-farm data were collected during 3 wk before calving up to 5 wk after calving. Blood samples were taken on d 3, 6, 9, and 21 after calving for determination of β-hydroxybutyrate (BHB), nonesterified fatty acids (NEFA), glucose, insulin-like growth factor-1 (IGF-1), insulin, and fructosamine. Milk samples were taken during the first 3 wk in lactation and analyzed by mid-infrared for fat, protein, lactose, urea, milk fatty acids, and BHB. Walking activity, feed intake, and body condition score (BCS) were monitored throughout the study. Linear mixed effect models were used to study the association between blood variables and (1) milk variables (i.e., milk models); (2) on-farm data (i.e., on-farm models) consisting of activity and dry matter intake analyzed during the dry period ([D]) and lactation ([L]) and BCS only analyzed during the dry period ([D]); and (3) the combination of both. In addition, to assess whether milk variables can clarify unexplained variation from the on-farm model and vice versa, Pearson marginal residuals from the milk and on-farm models were extracted and related to the on-farm and milk variables, respectively. The milk models had higher coefficient of determination (R2) than the on-farm models, except for IGF-1 and fructosamine. The highest marginal R2 values were found for BHB, glucose, and NEFA (0.508, 0.427, and 0.303 vs. 0.468, 0.358, and 0.225 for the milk models and on-farm models, respectively). Combining milk and on-farm data particularly increased R2 values of models assessing blood BHB, glucose, and NEFA concentrations with the fixed effects of the milk and on-farm variables mutually having marginal R2 values of 0.608, 0.566, and 0.327, respectively. Milk C18:1 was confirmed as an important milk variable in all models, but particularly for blood NEFA prediction. On-farm data were considerably more capable of describing the IGF-1 concentration than milk data (marginal R2 of 0.192 vs. 0.086), mainly due to dry matter intake before calving. The BCS [D] was the most important on-farm variable in relation to blood BHB and NEFA and could explain additional variation in blood BHB concentration compared with models solely based on milk variables. This study has shown that on-farm data combined with milk data can provide additional information concerning the metabolic health status of dairy cows. On-farm data are of interest to be further studied in predictive modeling, particularly because early warning predictions using milk data are highly challenging or even missing.

Milk fat miRNome changes in response to LPS challenge in Holstein cows

Mastitis is an inflammatory disease in dairy cows, causing economic losses and reducing animal welfare. In order to contribute for the discovery of early and noninvasive indicators, our objective was to determine the effects of a lipopolysaccharide (LPS) challenge on the microRNA profile (miRNome) of milk fat, using microarray analyses in cows. Cows were fed a lactation diet at ad libitum intake (n = 6). At 27 ± 3 days in milk, cows were injected with 50 µg of LPS Escherichia coli in one healthy rear mammary quarter. Milk samples were collected just before LPS challenge (LPS-) and 6.5 h after LPS challenge (LPS +) from the same cows. Microarray analysis was performed using customized 8 × 60 K ruminant miRNA microarrays to compare LPS- to LPS + miRNome. In silico functional analyses were performed using OmicsNet and Mienturnet software. MiRNome comparison between LPS- and LPS + identified 37 differentially abundant miRNAs (q-value ≤ 0.05). The predicted target genes of the 37 differentially abundant miRNAs are mostly involved in cell life including apoptosis, cell cycle, proliferation and differentiation and in gene expression processes. MiRNome analyses suggest that miRNAs profile is related to the inflammation response of the mammary gland. In conclusion, we demonstrated that milk fat might be an easy and rapid source of miRNAs that are potential indicators of early mastitis in cows.

Assessment of Mastitis Patterns in Serbian Dairy Cows: Blood Serum Metabolic Profile and Milk Composition Parameters

Mastitis is one of the most important diseases in dairy cows, leading to substantial economic losses associated with decreased milk production and quality. Early detection of changes in metabolic and milk parameters is crucial for maintaining animal welfare and milk quality. This study aimed to detect patterns in metabolic and milk composition parameters in Serbian dairy cows affected by mastitis. It also examined the relationship between these factors in cows with clinical and subclinical mastitis, as well as in healthy cows. This study included 60 Holstein-Friesian cows with the same body score condition that were in the same lactation phase. They were divided into three groups of 20: clinical and subclinical mastitis and a control group of healthy cows. The categorization was based on clinical udder health and the California mastitis test. Blood serum metabolic profiles were measured using a Rayto spectrophotometer (Shenzhen, China), and milk composition was determined using MilcoScanTM (Foss, Hilleroed, Denmark) and FossomaticTM (Foss, Hilleroed, Denmark) instruments. Significant increases in non-esterified fatty acids (NEFAs), beta-hydroxybutyrate (BHB), total protein, globulin, urea, total bilirubin, magnesium, and enzyme activity were noted in mastitis-affected cows compared to healthy ones. Additionally, mastitis-affected cows had higher total protein and globulin levels and increased somatic cell counts (SCCs), while albumin concentrations were decreased. Furthermore, a negative correlation between total protein and lactose suggested inflammation leading to reduced lactose levels due to cell damage, infection, and lactose use by mastitis pathogens. Hence, indicators of the energy and protein status of the metabolic profile, together with the chemical composition of milk, may be significant diagnostic tools for detecting, monitoring, and predicting the outcome of mastitis in cows.

Multivariate analysis of milk metabolite measures shows potential for deriving new resilience phenotypes

In a context of growing interest in breeding more resilient animals, a noninvasive indicator of resilience would be very valuable. We hypothesized that the time-course of concentrations of several milk metabolites through a short-term underfeeding challenge could reflect the variation of resilience mechanisms to such a challenge. We submitted 138 one-year-old primiparous goats, selected for extreme functional longevity (i.e., productive longevity corrected for milk yield [60 low longevity line goats and 78 high longevity line goats]), to a 2-d underfeeding challenge during early lactation. We measured the concentration of 13 milk metabolites and the activity of 1 enzyme during prechallenge, challenge, and recovery periods. Functional principal component analysis summarized the trends of milk metabolite concentration over time efficiently without preliminary assumptions concerning the shapes of the curves. We first ran a supervised prediction of the longevity line of the goats based on the milk metabolite curves. The partial least square analysis could not predict the longevity line accurately. We thus decided to explore the large overall variability of milk metabolite curves with an unsupervised clustering. The large year × facility effect on the metabolite concentrations was precorrected for. This resulted in 3 clusters of goats defined by different metabolic responses to underfeeding. The cluster that showed higher β-hydroxybutyrate, cholesterol, and triacylglycerols increase during the underfeeding challenge was associated with poorer survival compared with the other 2 clusters. These results suggest that multivariate analysis of noninvasive milk measures show potential for deriving new resilience phenotypes.

In-Line Registered Milk Fat-to-Protein Ratio for the Assessment of Metabolic Status in Dairy Cows

This study endeavors to ascertain alterations in the in-line registered milk fat-to-protein ratio as a potential indicator for evaluating the metabolic status of dairy cows. Over the study period, farm visits occurred biweekly on consistent days, during which milk composition (specifically fat and protein) was measured using a BROLIS HerdLine in-line milk analyzer (Brolis Sensor Technology, Vilnius, Lithuania). Clinical examinations were performed at the same time as the farm visits. Blood was drawn into anticoagulant-free evacuated tubes to measure the activities of GGT and AST and albumin concentrations. NEFA levels were assessed using a wet chemistry analyzer. Using the MediSense and FreeStyle Optium H systems, blood samples from the ear were used to measure the levels of BHBA and glucose in plasma. Daily blood samples were collected for BHBA concentration assessment. All samples were procured during the clinical evaluations. The cows were categorized into distinct groups: subclinical ketosis (SCK; n = 62), exhibiting elevated milk F/P ratios without concurrent clinical signs of other post-calving diseases; subclinical acidosis (SCA; n = 14), characterized by low F/P ratios (<1.2), severe diarrhea, and nondigestive food remnants in feces, while being free of other post-calving ailments; and a healthy group (H; n = 20), comprising cows with no clinical indications of illness and an average milk F/P ratio of 1.2. The milk fat-to-protein ratios were notably higher in SCK cows, averaging 1.66 (±0.29; p < 0.01), compared to SCA cows (0.93 ± 0.1; p < 0.01) and healthy cows (1.22). A 36% increase in milk fat-to-protein ratio was observed in SCK cows, while SCA cows displayed a 23.77% decrease. Significant differences emerged in AST activity, with SCA cows presenting a 26.66% elevation (p < 0.05) compared to healthy cows. Moreover, SCK cows exhibited a 40.38% higher NEFA concentration (p < 0.001). A positive correlation was identified between blood BHBA and NEFA levels (r = 0.321, p < 0.01), as well as a negative association between BHBA and glucose concentrations (r = -0.330, p < 0.01). Notably, AST displayed a robust positive correlation with GGT (r = 0.623, p < 0.01). In light of these findings, this study posits that milk fat-to-protein ratio comparisons could serve as a non-invasive indicator of metabolic health in cows. The connections between milk characteristics and blood biochemical markers of lipolysis and ketogenesis suggest that these markers can be used to check the metabolic status of dairy cows on a regular basis.

Mammary gland, kidney and rumen urea and uric acid transporters of dairy cows differing in milk urea concentration

The milk urea concentration (MUC) serves as indicator of urinary nitrogen emissions, but at comparable crude protein (CP) intake, cows with high (HMU) and low (LMU) MUC excrete equal urea amounts. We hypothesized that urea and uric acid transporters and sizes of the kidney, mammary gland, and rumen account for these phenotypes. Eighteen HMU and 18 LMU Holstein dairy cows fed a low (LP) and normal (NP) CP diet were studied. Milk, plasma and urinary urea concentrations were greater with NP feeding, while plasma and urinary urea concentrations were comparable between phenotypes. Milk and plasma uric acid concentrations were higher with LP feeding but not affected by phenotype. The milk-urine uric acid ratio was greater in HMU cows. The mRNA expressions of the ruminal urea transporter SLC14A1 and AQP10, the mammary gland and rumen AQP3, and the mammary gland uric acid transporter ABCG2 were not affected by group or diet. Renal AQP10, but not AQP3, AQP7, and SLC14A2 expressions, and the kidney weights were lower in HMU cows. These data indicate that renal size and AQP10 limit the urea transfer from blood to urine, and that MUC determines if uric acid is more released with milk or urine.

Glucose-6-Phosphate Dehydrogenase Activity in Milk May Serve as a Non-Invasive Metabolic Biomarker of Energy Balance in Postpartum Dairy Cows

Negative energy balance (EB) postpartum is associated with adverse outcomes in dairy cows; therefore, non-invasive biomarkers to measure EB are of particular interest. We determined whether specific metabolites, oxidative stress indicators, enzyme activity, and fatty acid (FA) profiles in milk can serve as indicators of negative EB. Forty-two multiparous Holstein dairy cows were divided at calving into 2 groups: one was milked 3 times daily and the other, twice a day for the first 30 d in milk (DIM). Cows were classified retrospectively as being in either negative EB (NEB, n = 19; the mean EB during the first 21 DIM were less than the overall median of -2.8 Mcal/d), or in positive EB (PEB, n = 21; the mean EB was ≥-2.8 Mcal/d). The daily milk yield, feed intake, and body weight were recorded individually. Blood samples were analyzed for metabolites and stress biomarkers. Milk samples were taken twice weekly from 5 to 45 DIM to analyze the milk solids, the FA profile, glucose, glucose-6-P (G6P), G6P-dehydrogenase (G6PDH) activity, malic and lactic acids, malondialdehyde (MDA), and oxygen radical antioxidant capacity (ORAC). The NEB cows produced 10.5% more milk, and consumed 7.6% less dry matter than the PEB cows. The plasma glucose concentration was greater and β-hydroxybutyrate was lower in the PEB vs. the NEB cows. The average concentrations of milk glucose, G6P, malic and lactic acids, and MDA did not differ between groups; however, the G6PDH activity was higher and ORAC tended to be higher in the milk of NEB vs. the PEB cows. The correlation between milk G6PDH activity and EB was significant (r = -0.39). The percentages of oleic acid and total unsaturated FA in milk were higher for the NEB vs. the PEB cows. These findings indicate that G6PDH activity in milk is associated with NEB and that it can serve as a non-invasive candidate biomarker of NEB in postpartum cows, that should be validated in future studies.

Animal board invited review: The contribution of adipose stores to milk fat: implications on optimal nutritional strategies to increase milk fat synthesis in dairy cows

A wide range of nutritional and non-nutritional factors influence milk fat synthesis and explain the large variation observed in dairy herds. The capacity of the animal to synthesize milk fat will largely depend on the availability of substrates for lipid synthesis, some of which originate directly from the diet, ruminal fermentation or from adipose tissue stores. The mobilization of non-esterified fatty acids from adipose tissues is important to support the energy demands of milk synthesis and will therefore have an impact on the composition of milk lipids, especially during the early lactation period. Such mobilization is tightly controlled by insulin and catecholamines, and in turn, can be affected indirectly by factors that influence these signals, namely diet composition, lactation stage, genetics, endotoxemia, and inflammation. Environmental factors, such as heat stress, also impact adipose tissue mobilization and milk fat synthesis, mainly through endotoxemia and an immune response-related increase in concentrations of plasma insulin. Indeed, as proposed in the present review, the central role of insulin in the control of lipolysis is key to improving our understanding of how nutritional and non-nutritional factors impact milk fat synthesis. This is particularly the case during early lactation, as well as in situations where mammary lipid synthesis is more dependent on adipose-derived fatty acids.

Effects of Environmental Temperature and Humidity on Milk Composition, Microbial Load, and Somatic Cells in Milk of Holstein Dairy Cows in the Northeast Regions of Iran

The present study aims to examine the relationships between temperature and humidity and milk composition, microbial load, and somatic cells in the milk of Holstein dairy cows. For this purpose, the temperature−humidity index, ambient temperature, and relative humidity data were obtained from the nearest weather stations. Production data were obtained from four dairy farms in Golestan province, Iran, collected from 2016 to 2021. The traits investigated were protein, fat, solids-not-fat (SNF), microbial load, and somatic cell count (SCC) in milk. The effects of the environmental temperature, humidity, month, and season on the milk composition, microbial load, and somatic cells were analyzed through analysis of variance. The effects of environmental temperature, humidity, month, and season on the milk composition, microbial load, and somatic cell composition were analyzed using a mixed procedure with a restricted maximum likelihood model. Although our findings revealed that there were significant differences in fat, protein, SNF, and SCC among the different months of the year (p < 0.01), no significant difference was observed in the total microbial count in milk. Environmental temperature presented significant impacts on fat, protein, SNF, SCC, and total microbial count within various temperature ranges (p < 0.01). When the temperature increased from 6.2 °C to 31.3 °C, the milk protein, fat, SNF, and somatic cell count significantly decreased, by approximately 4.09%, 5.75%, 1.31%, and 16.8%, respectively; meanwhile, the microbial count in milk significantly increased, by approximately 13.7%. Humidity showed an influence on fat, protein, non-fat solids, somatic cells, and total microbial count within different temperature ranges (p < 0.01). When the humidity increased from 54% to 82%, the milk protein, fat, SNF, and SCC significantly increased, by approximately 3.61%, 4.84%, 1.06%, and 10.2%, respectively; meanwhile, the microbial count in milk significantly decreased, by approximately 16.3%. The results demonstrate that there is a negative correlation between different months of the year, temperature, and the humidity of the environment, in terms of milk components and SCC. Our findings demonstrate that the optimum performance, in terms of milk composition, occurred in the first quarter of the year. As temperature increases and humidity decreases, milk quality decreases. Therefore, the adverse effects of environmental conditions on agricultural profits are not negligible, and strategies to better deal with the negative environmental effects are needed in order to improve milk quality in dairy cows.

Relationships between Milk and Blood Biochemical Parameters and Metabolic Status in Dairy Cows during Lactation

This study aimed to determine blood and milk metabolic parameters and their correlations for the purpose of evaluating metabolic status in dairy cows. Blood and milk samples were collected from 100 Holstein dairy cows during morning milking. The cows were allocated to four groups according to the production period, including cows in early (n = 18), full (n = 26), mid (n = 25) and late (n = 31) lactation. The value of non-esterified fatty acids (NEFA), β-hydroxybutyrate (BHB), glucose, triglycerides (TG), total cholesterol (TChol), total protein (TP), albumin, globulin, urea, total bilirubin (TBil), aspartate aminotransferase (AST), alanine aminotransferase (ALT), alkaline phosphatase (ALP), gamma-glutamyl transferase (GGT), and lactate dexydrogenase (LDH) in the blood were determined. The following milk parameters were measured: fat, protein, lactose, urea, AST, ALT, ALP, GGT, LDH and BHB. Blood serum NEFA, BHB, TBil, AST, ALT, ALP and LDH were higher in early lactation cows, whereas glucose, TP, globulin and urea levels were significantly lower in early lactation cows. Milk fat and lactose levels were lower in early lactation cows, whereas milk protein and the activities of AST, ALT, ALP and LDH in milk were highly greater in early lactation cows. Milk fat was positively correlated with glucose, TP and TG, and negatively correlated with BHB, NEFA, TBil, ALT, LDH and ALP levels in the blood. Enzyme activities in milk were positively correlated with those in blood and with blood NEFA, BHB and TBil levels, and negatively correlated with blood glucose, TChol and TG. A significant positive correlation existed between blood and milk BHB values. Many correlations showed the same slope during all lactation periods. In conclusion, similar changes in blood and milk metabolite concentration during lactation and milk to blood correlations confirm that milk has great potential in predicting of blood metabolites and metabolic status of cows.

Fluorometric determination of isocitrate dehydrogenase (EC 1.1.1.42; 1; NADP+ dependent) in ruminant milk

The enzyme isocitrate dehydrogenase (EC 1.1.1.42; 1; NADP⁺ dependent) located in the mammary cell cytosol mediates the synthesis of the majority of reducing equivalents for the energetically demanding milk fat and cholesterol synthesis in mammary cell cytosol. The present article presents a novel fluorometric method for quantification of the activity of this enzyme (IDH) in ruminant milk without pretreatment of the sample. Further, 493 goat milk samples - harvested before, during and after a nutritional restriction - were analysed for IDH activity i) with addition of extra substrate (isocitrate), and ii) with the intrinsic isocitrate solely. The IDH activity ranged from 0.22 to 15.4 units [nano moles product/(ml * min)] (un-supplemented) and from 0.22 to 45.6 units (isocitrate supplemented). The IDH activity increased considerably in milk during the nutritional restriction period concomitant with the increase in the metabolite isocitrate concentration and somatic cell count and returned to the initial level shortly after restriction period. The present 'high through-put' analytical method may be beneficial in future studies to phenotype modifications in mammary energy metabolism and milk fat synthesis, for which IDH activity may be a biomarker.

Combined biotin, folic acid, and vitamin B12 supplementation given during the transition period to dairy cows: Part II. Effects on energy balance and fatty acid composition of colostrum and milk

Biotin (B₈), folate (B₉), and vitamin B₁₂ (B₁₂) are involved in several metabolic reactions related to energy metabolism. We hypothesized that a low supply of one of these vitamins during the transition period would impair metabolic status. This study was undertaken to assess the interaction between B₈ supplement and a supplementation of B₉ and B₁₂ regarding body weight (BW) change, dry matter intake, energy balance, and fatty acid (FA) compositions of colostrum and milk fat from d -21 to 21 relative to calving. Thirty-two multiparous Holstein cows housed in tie stalls were randomly assigned, according to their previous 305-d milk yield, to 8 incomplete blocks in 4 treatments: (1) a 2-mL weekly i.m. injection of saline (0.9% NaCl; B₈-/B₉B₁₂-); (2) 20 mg/d of dietary B₈ (unprotected from ruminal degradation) and 2-mL weekly i.m. injection of 0.9% NaCl (B₈+/B₉B₁₂-); (3) 2.6 g/d of dietary B₉ (unprotected) and 2-mL weekly i.m. injection of 10 mg of B₁₂ (B₈-/B₉B₁₂+); (4) 20 mg/d of dietary B₈, 2.6 g/d of dietary B₉, and 2-mL weekly i.m. injection of 10 mg of B₁₂ (B₈+/B₉B₁₂+) in a 2 × 2 factorial arrangement. Colostrum was sampled at first milking. and milk samples were collected weekly on 2 consecutive milkings and analyzed for FA composition. Body condition score and BW were recorded every week throughout the trial. Within the first 21 d of lactation, B₈-/B₉B₁₂+ cows had an increased milk yield by 13.5% [45.5 (standard error, SE: 1.8) kg/d] compared with B₈-/B₉B₁₂- cows [40.1 (SE: 1.9)], whereas B₈ supplement had no effect. Even though body condition score was not affected by treatment, B₈-/B₉B₁₂+ cows had greater BW loss by 24 kg, suggesting higher mobilization of body reserves. Accordingly, milk de novo FA decreased and preformed FA concentration increased in B₈-/B₉B₁₂+ cows compared with B₈-/B₉B₁₂- cows. In addition, cows in the B₈+/B₉B₁₂- group had decreased milk de novo FA and increased preformed FA concentration compared with B₈-/B₉B₁₂- cows. Treatment had no effect on colostrum preformed FA concentration. Supplemental B₈ decreased concentrations of ruminal biohydrogenation intermediates and odd- and branched-chain FA in colostrum and milk fat. Moreover, postpartum dry matter intake for B₈+ cows tended to be lower by 1.6 kg/d. These results could indicate ruminal perturbation caused by the B₈ supplement, which was not protected from rumen degradation. Under the conditions of the current study, in contrast to B₈+/B₉B₁₂- cows, B₈-/B₉B₁₂+ cows produced more milk without increasing dry matter intake, although these cows had greater body fat mobilization in early lactation as suggested by the FA profile and BW loss.