Evaluation of eight cow-side ketone tests in milk for detection of subclinical ketosis in dairy cows

Comparison of Two Methods for the Measurement of Blood Plasma and Capillary Blood Glucose in Tropical Highland Grassing Dairy Cows

(1) Background: There is lack of published studies validating specific cow-side glucometers such as Centrivet GK (CVGK). (2) Methods: The aims were (1) to measure and compare the blood glucose concentrations in 52 tropic highland grassing cows by using CVGK and the traditional enzymatic/photometric assay (EPA) in plasma and serum (reference method) and (2) to establish if glucose concentrations obtained via these methods could be affected by several demographic and zootechnical parameters of the dairy herd evaluated. (3) Results: Glucose concentrations were significantly (p = 0.00) affected by the method used for their measurement. The intra-assay coefficient of variation (CV) for glucose concentrations in plasma EPA and for CVGK was 14% for both methods with serum EPA, whereas the inter-assay CV for plasma EPA and CVGK was 8% and 13.7%, respectively, with serum EPA. Pearson correlation coefficient calculations between the reference method in serum and plasma presented a slightly positive significant (p = <0.000) correlation (r = 0.56), whereas there was not a significant (p = 0.413) correlation between serum EPA and CVGK (r = 0.135). The Passing and Bablok regressions were out of the ideal expected values for the slope (β = 1) and the intercept (α = 0) (11), whereas the Bland-Altman plots showed a bias of 5.29 ± 11.73 (mg/dL) for serum and plasma and 11.01 ± 15.74 (mg/dL) for serum and CVGK. The ROC curve showed no sensitivity in detecting normoglycemic cows (area = 53.7 %, e.d = 12.5 %, p = 0.759) for CVGK when compared to plasma EPA (area = 36.1 %, e.d = 14.2 %, p = 0.256). Plasma EPA exhibited a better but not significant effect in detecting hyperglycemic cows (area = 63.9%, e.d = 14.2%, p = 0.256) when compared to HHD (area = 46.3 %, e.d = 12.5 %, p = 0.759). General glucose concentrations, independently of the method used, were significantly (p = <0.001) greater in young cows when compared to adult and old cows. (4) Conclusions: Glucose concentration measurement in plasma by using EPA or in capillary blood via CVGK were not reliable methods when compared with the reference method.

Milk β-hydroxybutyrate metrics and its consequences for surveillance of hyperketonaemia on commercial dairy farms

Dairy cows that are unable to adapt to a change in their metabolic status are at risk for hyperketonaemia (HK). Reported HK herd level prevalences range a lot and we hypothesized that this is partly due to differences in used tests and monitoring protocols. Insights in milk β-hydroxybutyrate (BHB) metrics can potentially explain why the reported incidences or prevalences vary between test strategies. Automated collection and repeated analyses of individual milk samples with the DeLaval Herd Navigator™ (HN) provides real-time data on milk BHB concentrations. We aimed to use that information to gain insight in BHB metrics measured in milk from 3 to 60 days in milk (DIM). Using different cut-offs (0.08, 0.10 and 0.15 mmol/L), 5 BHB metrics were determined. Furthermore, the impact of 4 arbitrary test protocols on the detected incidence of HK was assessed. We used HN data of 3,133 cows from 35 herds. The cumulative incidence of HK between 3 and 60 DIM varied between 30.5 and 76.7% for different cut-off values. We found a higher HK incidence for higher parity cows. The first elevated BHB concentrations were roughly found between one and two weeks after calving. For higher parity cows the maximum BHB concentrations were higher, the onset of HK was earlier after calving, and the number of episodes of HK was higher. It appeared that the sensitivity of a HK test protocol can be increased by increasing the testing frequency from once to twice a week. Also extending the number of days of the test window from 4-14 to 4-21 days enhances the chance to find cows experiencing HK. In conclusion, HN data provided useful insights in milk BHB metrics. The chosen cut-off value had a large effect on the reported metrics which explains why earlier reported incidences or prevalences vary such a lot. Differences in test period and sample selection also had a large impact on the observed HK incidence. We suggest to take this in consideration while evaluating whether HK is an issue on farm level and use a uniform protocol for benchmarking of HK between farms.

A systematic approach to analyse the impact of farm-profiles on bovine health

In this study we present systematic framework to analyse the impact of farm profiles as combinations of environmental conditions and management practices on common diseases in dairy cattle. The data used for this secondary data analysis includes observational data from 166 farms with a total of 5828 dairy cows. Each farm is characterised by features from five categories: husbandry, feeding, environmental conditions, housing, and milking systems. We combine dimension reduction with clustering techniques to identify groups of similar farm attributes, which we refer to as farm profiles. A statistical analysis of the farm profiles and their related disease risks is carried out to study the associations between disease risk, farm membership to a specific cluster as well as variables that characterise a given cluster by means of a multivariate regression model. The disease risks of five different farm profiles arise as the result of complex interactions between environmental conditions and farm management practices. We confirm previously documented relationships between diseases, feeding and husbandry. Furthermore, novel associations between housing and milking systems and specific disorders like lameness and ketosis have been discovered. Our approach contributes to paving a way towards a more holistic and data-driven understanding of bovine health and its risk factors.

Analytical Methodology for a Metabolome Atlas of Goat's Plasma, Milk and Feces Using 1H-NMR and UHPLC-HRMS

Metabolomics has been increasingly used in animal and food sciences. Animal health is one of the most important factor that can also alter animal integrity and welfare. Some studies have already investigated the link between health and metabolic profile of dairy animals. These studies in metabolomics often consider a single type of sample using a single analytical platform (nuclear magnetic resonance or mass spectrometry). Only few studies with multi-platform approaches are also used with a single or a multi type of sample, but they mainly consider dairy cows’ metabolome although dairy goats present similar diseases, that it could be interesting to detect early to preserve animal health and milk production. This study aims to create a metabolic atlas of goat plasma, milk and feces, based on healthy animals. Our study describes a standard operating procedure for three goat matrices: blood plasma, milk, and feces using multiple platforms (NMR (¹H), UHPLC (RP)-MS and UHPLC (HILIC)-MS) that follows a unique sample preparation procedure for each sample type to be analyzed on multi-platforms basis. Our method was evaluated for its robustness and allowed a better characterization of goat metabolic profile in healthy conditions.

Abomasal infusion of corn starch and β-hydroxybutyrate in early-lactation Holstein-Friesian dairy cows to induce hindgut and metabolic acidosis

The objectives of this study were to induce hindgut and metabolic acidosis via abomasal infusion of corn starch and β-hydroxybutyrate (BHB), respectively, and to determine the effects of these physiological states in early-lactation dairy cows. In a 6 × 6 Latin square design, 6 rumen-fistulated Holstein-Friesian dairy cows (66 ± 18 d in milk) were subjected to 5 d of continuous abomasal infusion treatments followed by 2 d of rest. The abomasal infusion treatments followed a 3 × 2 factorial design, with 3 levels of corn starch and 2 levels of BHB. The infusions were water as control, 1.5 kg of corn starch/d, 3.0 kg of corn starch/d, 8.0 mol BHB/d, 1.5 kg of corn starch/d + 8.0 mol BHB/d, or 3.0 kg of corn starch/d + 8.0 mol BHB/d. A total mixed ration consisting of 35.0% grass silage, 37.4% corn silage, and 27.6% concentrate (on a dry matter basis) was fed at 90% of ad libitum intake of individual cows. The experiment was conducted in climate respiration chambers to facilitate determination of energy and N balance. Fecal pH decreased with each level of corn starch infused into the abomasum and was 6.49, 6.00, and 5.15 with 0.0, 1.5, and 3.0 kg of corn starch/d, respectively, suggesting that hindgut acidosis was induced with corn starch infusion. No systemic inflammatory response was observed and the permeability of the intestine or hindgut epithelium was not affected by the more acidic conditions. This induced hindgut acidosis was associated with decreased digestibility of nutrients, except for crude fat and NDF, which were not affected. Induced hindgut acidosis did not affect milk production and composition and energy balance, but increased milk N efficiency. Abomasal infusion of BHB resulted in a compensated metabolic acidosis, which was characterized by a clear disturbance of acid-base status (i.e., decreased blood total CO₂, HCO₃, and base excess, and a tendency for decreased urinary pH), whereas blood pH remained within a physiologically normal range. Abomasal infusion of BHB resulted in increased concentrations of BHB in milk and plasma, but both remained well below the critical threshold values for subclinical ketosis. Induced compensated metabolic acidosis, as a result of abomasally infused BHB, increased energy retained as body fat, did not affect milk production and composition or inflammatory response, but increased intestinal permeability.

Short communication: Prediction of hyperketonemia in dairy cows in early lactation using on-farm cow data and net energy intake by partial least square discriminant analysis

The objectives of this study were (1) to evaluate if hyperketonemia in dairy cows (defined as plasma β-hydroxybutyrate ≥1.0 mmol/L) can be predicted using on-farm cow data either in current or previous lactation week, and (2) to study if adding individual net energy intake (NEI) can improve the predictive ability of the model. Plasma β-hydroxybutyrate concentration, on-farm cow data (milk yield, percentage of fat, protein and lactose, fat- and protein-corrected milk yield, body weight, body weight change, dry period length, parity, and somatic cell count), and NEI of 424 individual cows were available weekly through lactation wk 1 to 5 postpartum. To predict hyperketonemia in dairy cows, models were first trained by partial least square discriminant analysis, using on-farm cow data in the same or previous lactation week. Second, NEI was included in models to evaluate the improvement of the predictability of the models. Through leave-one trial-out cross-validation, models were evaluated by accuracy (the ratio of the sum of true positive and true negative), sensitivity (68.2% to 84.9%), specificity (61.5% to 98.7%), positive predictive value (57.7% to 98.7%), and negative predictive value (66.2% to 86.1%) to predict hyperketonemia of dairy cows. Through lactation wk 1 to 5, the accuracy to predict hyperketonemia using data in the same week was 64.4% to 85.5% (on-farm cow data only), 66.1% to 87.0% (model including NEI), and using data in the previous week was 58.5% to 82.0% (on-farm cow data only), 59.7% to 85.1% (model including NEI). An improvement of the accuracy of the model due to including NEI ranged among lactation weeks from 1.0% to 4.4% when using data in the same lactation week and 0.2% to 6.6% when using data in the previous lactation week. In conclusion, trained models via partial least square discriminant analysis have potential to predict hyperketonemia in dairy cows not only using data in the current lactation week, but also using data in the previous lactation week. Net energy intake can improve the accuracy of the model, but only to a limited extent. Besides NEI, body weight, body weight change, milk fat, and protein content were important variables to predict hyperketonemia, but their rank of importance differed across lactation weeks.

Ketosis an Old Story Under a New Approach

Ketosis, characterized by high concentrations of ketone bodies in the blood, urine, and milk, affects a considerable number of cows immediately after calving. Although much is known about ketosis, dairy cows continue to be affected in every herd world-wide. Cows affected by ketosis are treated with palliative treatments after the disease is diagnosed. This is a very expensive approach and costs the dairy industry extra expenses, contributing to lower profitability of dairy herds. In this review article, we summarize the mainstream view on ketosis, classification of ketosis into three types, current diagnostic approaches to ketosis, and the economic impact of ketosis on dairy farms. Additionally, we discuss the most recent applications of the new ‘omics’ science of metabolomics in studying the etiopathology of ketosis as well as its contribution in identification of novel screening or diagnostic biomarkers of ketosis.

Identification of Plasma Fatty Acids in Four Lipid Classes to Understand Energy Metabolism at Different Levels of Ketonemia in Dairy Cows Using Thin Layer Chromatography and Gas Chromatographic Techniques (TLC-GC)

Excessive mobilization of adipose tissue in high milk producing dairy cows predisposes to metabolic diseases. The aim of this research was to identify the plasma fatty acids in four lipid classes as biomarkers for the diagnosis of hyperketonemia in bovines using thin layer chromatography and gas chromatographic techniques (TLC-GC). Sixty multiparous Holstein-Friesian dairy cows were enrolled in the study. Blood samples from the coccygeal vein were collected and β-hydroxybutyrate (BHB) was evaluated. All animals were divided into three groups on the basis of ketonemia: BHB < 0.50 mmol/L, 0.50 < BHB < 1.0 mmol/L, and BHB > 1.0 mmol/L. Plasma fatty acid concentrations were evaluated in four lipid classes: Free Fatty Acids (FFA), Triglycerides (TG), Cholesterol Esters (CE) And Phospholipids (PL). The concentration of fatty acids was analyzed using TLC-GC. The results showed the following significance in the lipid classes: 19 fatty acids were significant (p < 0.053) in FFA, nine fatty acids were significant (p < 0.050) in TG, eight fatty acids were significant (p < 0.050) in CE and three fatty acids were significant (p < 0.049) in PL. Eleven parameters were considered as predictive fatty acids related to animals in hyperketonemia. The FFA increased simultaneously with blood BHB levels, although the identified predictive fatty acids related to the TG and CE lipid classes decreased, meanwhile the BHB values increased. In the PL lipid class, no fatty acids were predictive.

Associations between motion activity, ketosis risk and estrus behavior in dairy cattle

Ketosis (acetonaemia) is a metabolic disorder that occurs in cattle when energy demand exceeds energy intake and results in a negative energy balance. The course of the disease often starts with a subclinical phase, so early detection is crucial for decisive strategies. The aim of this study was to determine whether daily motion activity could be used as an indicator of subclinical ketosis in early lactation and to evaluatethe effect of subclinical ketosis on activity at estrus. The study was carried out on a 75-cow dairy farm over 6 months. Data were collected from 48 cows between day 0 and day 70 post-partum. Beta-Hydroxybutyrate (BHB) concentrations were evaluated in milk samples using rapid on-site ketosis tests. A test was considered positive at a concentration of >100 μmol/l. The animals were divided into two groups: group 'Healthy' (H) and group 'Ketosis' (K). Once the on-site test was positive, the cows were assigned to group K. Progesterone concentrations were evaluated in milk by photometric detection of the colour reaction of a competitive, heterologous enzyme immunoassay (EIA). Each drop from ≥0.3 ng/ml to <0.3 ng/ml with a subsequent increase to ≥0.3 ng/ml was considered estrus. Daily milk yield, concentrate intake and motion activity were recorded from a computerized dairy management system with the associated software (DairyPlan C21). Animals in group K had lower average daily activity levels than animals in group H. In this study, statistically significant reduced motion activity in animals in group K was observed on days 6-12 post-partum (P < 0.001, χ² test) compared with the herd mean daily motion activity. Furthermore, a significant association could be found between motion activity and group affiliation (logistic regression models). The sensitivity of the detection of cows at risk for ketosis was 81.8 %, and the specificity was 65.4 %, retrospectively determined by their activity behaviour. The mean motion activity on the day of estrus was significantly (P < 0.05) lower in animals in group K than in those in group H. This method may help to establish a future early warning system for the risk of ketosis in dairy cows. Thus, cows at risk may be identified for further targeted diagnostics and for selective treatment procedures. This study confirms the already reported lasting effect of subclinical ketosis on reproductive efficiency.

The relationship between plasma β-hydroxybutyric acid and conjugated linoleic acid in milk as a biomarker for early diagnosis of ketosis in postpartum Polish Holstein-Friesian cows

Background

The aim of this study was to investigate the association between plasma β-hydroxybutyric acid (BHBA) and conjugated linoleic acid in postpartum Polish Holstein-Friesian (PHF) cows. The experiment was carried out at an experimental dairy farm, where a herd of approximately 350 cows was kept. Samples were taken at six time points: between days 5–7, 8–14, 15–21, 22–28, 29–35, and 36–42, resulting in 510 samples of both milk and blood. The cows involved in the experiment were divided into two groups – ketotic and healthy – by taking into account general health symptoms, blood serum BHBA, and non-esterified fatty acids (NEFA) concentration at 5–7 days postpartum.

Results

In the first week of lactation, at 5–7 day in milk (DIM), the study showed a 53% lower level of C18:2 cis-9 trans-11 (CLA9) and an 80% lower level of C18:2 trans-10 cis-12 (CLA10) in cows with diagnosed ketosis compared to healthy cows. In the second week of lactation (8–14 DIM), a 34% lower level of CLA9 and a 54% lower level of CLA10 was found in the group of cows with BHBA levels > 1.2 mmol/L. Additionally, Pearson correlation analysis showed significant negative correlation between BHBA x CLA9 and BHBA x CLA10 in the first week of lactation: − 0.732and − 0.821, respectively.

Conclusion

The study shows that that both CLA9 and CLA10 can be used as markers for the early diagnosis of elevated blood levels of BHBA in postpartum Polish Holstein-Friesian cows.

Review: Metabolic challenges in lactating dairy cows and their assessment via established and novel indicators in milk

The increasing lactational performance of dairy cows over the last few decades is closely related to higher nutritional requirements. The decrease in dry matter intake during the peripartal period results in a considerable mobilisation of body tissues (mainly fat reserves and muscle mass) to compensate for the prevailing lack of energy and nutrients. Despite the activation of adaptive mechanisms to mobilise nutrients from body tissues for maintenance and milk production, the increased metabolic load is still a risk factor for animal health. The prevalence of production diseases, particularly subclinical ketosis is high in the early lactation period. Increased β-hydroxybutyrate (BHB) concentrations further depress gluconeogenesis, feed intake and the immune system. Despite a variety of adaptation responses to nutrient and energy deficit that exists among dairy cows, an early and non-invasive detection of developing metabolic disorders in milk samples would be useful. The frequent and regular milking process of dairy cows creates the ability to obtain samples at any stage of lactation. Routine identification of biomarkers accurately characterising the physiological status of an animal is crucial for decisive strategies. The present overview recapitulates established markers measured in milk that are associated with metabolic health of dairy cows. Specifically, measurements of milk fat, protein, lactose and urea concentrations are evaluated. Changes in the ratio of milk fat to protein may indicate an increased risk for rumen acidosis and ketosis. The costly determination of individual fatty acids in milk creates barriers for grouping of fatty acids into saturated, mono- and polyunsaturated fatty acids. Novel approaches include the potential of mid-IR (MIR) based predictions of BHB and acetone in milk, although the latter are not directly measured, but only estimated via indirect associations of concomitantly altered milk composition during (sub)clinical ketosis. Although MIR-based ketone body concentrations in milk are not suitable to monitor the metabolic status of the individual cow, they provide an estimate of the overall herd or specific groups of animals earlier in a particular stage of lactation. Management decisions can be made earlier and animal health status improved by adjusting diet composition.

Circulating electrolytes in the bloodstream of transition Sarda goats make the difference in body fluid distribution between single vs. twin gestation

Health of transition goats can be explored through the assessment of the metabolic profile. Selected circulating parameters of clinical importance may be used for indicating homeostasis perturbations during the transition period. The present study aimed to characterize the metabolic profile of transition dairy goats raised under a pasture-based system to determine whether the conceptus number (single vs. twin gestation) influences circulating concentrations of different parameters in the bloodstream, including nutrient- and fluid-related metabolites for health assessment. Sarda dairy goats diagnosed for single (n = 5) or twin gestation (n = 6) were selected out of a flock of 156 heads and sampled for blood weekly, throughout one month before and one month after kidding. Significantly (p < .01), differences in blood serum metabolite concentrations before and after birthing were observed in both groups of animals, as to glucose (Glu), total cholesterol (TC) and triglycerides (TG), total protein (TP) and Urea. Circulating creatinine (Crea) turned out to differ significantly (p < .01) between single vs. twin kidding does. Significant correlations (p < .001) between groups of circulating electrolytes (Na, Chloride, Mg, K) were also observed and this datum was assumed to change following the different distribution of fluids in the body of the doe, in the last third of gestation, at birthing and during early lactation, respectively. Interestingly, electrolyte levels in this trial displayed different concentrations in the bloodstream according to physiological stage and kid numbers.

Survey of ketolactia, determining the main predisposing management factors and consequences in Hungarian dairy herds by using a cow-side milk test

The aims of the survey were to determine the prevalence of ketosis in dairy herds by measuring the concentration of beta-hydroxybutyrate (BHBA) in milk by Keto-Test (Sanwa Kagaku Kenkyusho, Nagoya, Japan); risk factors and the relationship with postpartum diseases were investigated. 1667 early lactating (days in milk 0-75) cows were tested in 52 dairy herds in 2013 and 2014 years. In total, 29.3 per cent of samples were positive (BHBA_MILK ≥100 µmol/l), including 3.7 per cent high positives (BHBA_MILK ≥500 µmol/l). The prevalence was similar in herds with less than or more than 9000 kg milk yield (0.34 and 0.38, respectively, P=0.4); however, it was higher in the herds with more than 1000 cows than in smaller herds (<500 and 500-1000 cows) (0.46, P=0.03). The BHBA level in milk was in a non-linear positive relationship with parity (P=0.01), associated with retained placenta (P=0.0006), mastitis (P=0.02) and clinical ketosis (P<0.001). The results confirm the high prevalence of ketolactia in Hungarian dairy herds and its links to herd-related and cow-related risk factors and diseases occurring commonly in fresh cows.

A randomized controlled trial on the effect of incomplete milking during early lactation on ketonemia and body condition loss in Holstein dairy cows

Limiting milk production for a short period of time in early lactation could be a relevant strategy to prevent hyperketonemia (HYK). From December 2013 to March 2015, 838 multiparous Holstein cows from 13 herds were enrolled in a randomized controlled trial evaluating the effect of incomplete milking in early lactation on ketonemia and its effect on body condition score (BCS) loss. Cows were randomly assigned 4 wk before expected calving date to 1 of 2 treatment groups, (1) a conventional milking protocol (CON) for which cows were completely milked or (2) an incomplete milking protocol (INC) for which a maximum of 10 to 14 kg of milk/d were withdrawn during the first 5 d in milk (DIM). β-Hydroxybutyrate (BHB) concentrations were measured from blood samples collected on each cow 3 times at weekly intervals. Hyperketonemia was defined as BHB ≥1.4 mmol/L. Body condition score variation in the postcalving period was calculated by subtracting BCS assessed at wk 7 from BCS assessed at first week after calving. Effect of treatment on ketonemia and prevalence of HYK were evaluated for 4 specific time periods: 1 to 3, 4 to 7, 8 to 17, and 18 to 26 DIM. Effect of treatment on ketonemia was investigated using linear mixed models with natural logarithm of BHB measurements as outcome and treatment groups as fixed effect. Generalized linear mixed models with HYK as outcome, using logit link, and treatment groups as fixed effect were used to investigate effect of treatment on odds of HYK. A logistic regression model with BCS loss (<0.75 or ≥0.75) as outcome and treatment groups and herd as fixed effects was used to study effect of INC on odds of having BCS loss ≥0.75. A total of 813 lactations had complete data and were used for statistical analysis of ketonemia and HYK. A total of 709 lactations had complete data and were used for analysis of BCS loss. Geometric means of blood BHB concentrations during the 1 to 3, 4 to 7, 8 to 17, and 18 to 26 DIM periods were, respectively, 0.72 (95% confidence interval = 0.66, 0.80), 0.66 (0.60, 0.73), 0.90 (0.80, 1.01), and 0.93 (0.83, 1.05) mmol/L for INC, and 0.65 (0.59, 0.72), 0.79 (0.72, 0.87), 0.94 (0.84, 1.06), and 0.92 (0.82, 1.04) mmol/L for CON. Cows in INC group had lower ketonemia during the 4 to 7 DIM period. Predicted prevalence of HYK during the 1 to 3, 4 to 7, 8 to 17, and 18 to 26 DIM periods were, respectively, 2.8 (3.2, 15.1), 4.6 (2.0, 10.0), 13.4 (8.4, 20.0), and 23.0% (17.4, 29.7) for INC and 2.6 (2.5, 13.8), 10.7 (5.6, 19.3), 19.4 (13.0, 27.9), and 21.3% (16.0, 27.8) for CON. The INC treatment reduced the prevalence of HYK during the 4 to 7 and 8 to 17 DIM periods. No association was observed between INC and BCS loss in the postcalving period. Overall, the incomplete milking protocol was effective for reducing ketonemia and prevalence of HYK during the early postpartum period.

Predicting hyperketonemia by logistic and linear regression using test-day milk and performance variables in early-lactation Holstein and Jersey cows

Although cowside testing strategies for diagnosing hyperketonemia (HYK) are available, many are labor intensive and costly, and some lack sufficient accuracy. Predicting milk ketone bodies by Fourier transform infrared spectrometry during routine milk sampling may offer a more practical monitoring strategy. The objectives of this study were to (1) develop linear and logistic regression models using all available test-day milk and performance variables for predicting HYK and (2) compare prediction methods (Fourier transform infrared milk ketone bodies, linear regression models, and logistic regression models) to determine which is the most predictive of HYK. Given the data available, a secondary objective was to evaluate differences in test-day milk and performance variables (continuous measurements) between Holsteins and Jerseys and between cows with or without HYK within breed. Blood samples were collected on the same day as milk sampling from 658 Holstein and 468 Jersey cows between 5 and 20 d in milk (DIM). Diagnosis of HYK was at a serum β-hydroxybutyrate (BHB) concentration ≥1.2 mmol/L. Concentrations of milk BHB and acetone were predicted by Fourier transform infrared spectrometry (Foss Analytical, Hillerød, Denmark). Thresholds of milk BHB and acetone were tested for diagnostic accuracy, and logistic models were built from continuous variables to predict HYK in primiparous and multiparous cows within breed. Linear models were constructed from continuous variables for primiparous and multiparous cows within breed that were 5 to 11 DIM or 12 to 20 DIM. Milk ketone body thresholds diagnosed HYK with 64.0 to 92.9% accuracy in Holsteins and 59.1 to 86.6% accuracy in Jerseys. Logistic models predicted HYK with 82.6 to 97.3% accuracy. Internally cross-validated multiple linear regression models diagnosed HYK of Holstein cows with 97.8% accuracy for primiparous and 83.3% accuracy for multiparous cows. Accuracy of Jersey models was 81.3% in primiparous and 83.4% in multiparous cows. These results suggest that predicting serum BHB from continuous test-day milk and performance variables could serve as a valuable diagnostic tool for monitoring HYK in Holstein and Jersey herds.

Evaluation of a point-of-care electrochemical meter to detect subclinical ketosis and hypoglycaemia in lactating dairy cows

OBJECTIVES

To evaluate and validate a hand-held electrochemical meter (Precision Xtra®) as a screening test for subclinical ketosis and hypoglycaemia in lactating dairy cattle.

DESIGN

Method comparison study using a convenience sample.

PROCEDURE

Blood samples were collected into plain tubes from the coccygeal vessels of 181 Holstein cows at 2-4 weeks of lactation during summer in Iran. Blood β-hydroxybutyrate concentration (BHB) and glucose concentration were immediately measured by the electrochemical meter after applying 20 μL of blood to the reagent strip. Passing-Bablok regression and Bland-Altman plots were used to determine the accuracy of the meter against laboratory reference methods (BHB dehydrogenase and glucose oxidase).

RESULTS

Serum BHB ranged from 0.1 to 7.3 mmol/L and serum glucose ranged from 0.9 to 5.1 mmol/L. Passing-Bablok regression analysis indicated that the electrochemical meter and reference methods were linearly related for BHB and glucose, with a slope estimate that was not significantly different from 1.00. Clinically minor, but statistically significant, differences were present for the intercept value for Passing-Bablok regression analysis for BHB and glucose, and bias estimates in the Bland-Altman plots for BHB and glucose.

CONCLUSION

The electrochemical meter provided a clinically useful method to detect subclinical ketosis and hypoglycaemia in lactating dairy cows. Compared with other method validation studies using the meter, we attributed the improved performance of the electrochemical meter to application of a fixed volume of blood (20 μL) to the reagent strip, use of the meter in hot ambient conditions and use of glucose oxidase as the reference method for glucose analysis.

How dietary cottonseed hull affects the performance of young bulls finished in a high-concentrate system

This study aimed to evaluate the effects of diets composed of cottonseed hull on feed intake, in vitro digestibility, animal performance, carcass characteristics and ingestive behaviour of young bulls (1/2 Simmental × 1/2 Nellore) fed in a high-concentrate system. Thirty crossbred young bulls (319 ± 12.5 kg of bodyweight, and 11 ± 0.8 months old) were assigned to a complete randomised experimental design of three diets (CH21: cottonseed hull 210 g/kg on a DM, basis; CH27: cottonseed hull 270 g/kg on a DM basis; CH33: cottonseed hull 330 g/kg on a DM basis) with 10 animals per group. The animals were kept in a feedlot for 162 days. The cottonseed hull diets had effects on DM intake and neutral detergent fibre intake. The CH21 diet reduced the DM and detergent fibre intakes (kg/day and kg/100 kg bodyweight) and increased the in vitro digestibility of DM. However, the in vitro digestibility of neutral detergent fibre was greater with the CH27 diet. The cottonseed hull diets did not have effects on animal performance or the carcass characteristics of young bulls. However, the CH33 diet reduced the feed efficiency of the animals. A cottonseed hull level up to 270 g/kg of dietary DM may be utilised as a non-forage fibre in high-concentrate diets for young bulls in feedlots.

Invited review: Opportunities for genetic improvement of metabolic diseases

Metabolic disorders are disturbances to one or more of the metabolic processes in dairy cattle. Dysfunction of any of these processes is associated with the manifestation of metabolic diseases or disorders. In this review, data recording, incidences, genetic parameters, predictors, and status of genetic evaluations were examined for (1) ketosis, (2) displaced abomasum, (3) milk fever, and (4) tetany, as these are the most prevalent metabolic diseases where published genetic parameters are available. The reported incidences of clinical cases of metabolic disorders are generally low (less than 10% of cows are recorded as having a metabolic disease per herd per year or parity/lactation). Heritability estimates are also low and are typically less than 5%. Genetic correlations between metabolic traits are mainly positive, indicating that selection to improve one of these diseases is likely to have a positive effect on the others. Furthermore, there may also be opportunities to select for general disease resistance in terms of metabolic stability. Although there is inconsistency in published genetic correlation estimates between milk yield and metabolic traits, selection for milk yield may be expected to lead to a deterioration in metabolic disorders. Under-recording and difficulty in diagnosing subclinical cases are among the reasons why interest is growing in using easily measurable predictors of metabolic diseases, either recorded on-farm by using sensors and milk tests or off-farm using data collected from routine milk recording. Some countries have already initiated genetic evaluations of metabolic disease traits and currently most of these use clinical observations of disease. However, there are opportunities to use clinical diseases in addition to predictor traits and genomic information to strengthen genetic evaluations for metabolic health in the future.

Polymorphisms within the APOBR gene are highly associated with milk levels of prognostic ketosis biomarkers in dairy cows

Essentially all high-yielding dairy cows experience a negative energy balance during early lactation leading to increased lipomobilization, which is a normal physiological response. However, a severe energy deficit may lead to high levels of ketone bodies and, subsequently, to subclinical or clinical ketosis. It has previously been reported that the ratio of glycerophosphocholine to phosphocholine in milk is a prognostic biomarker for the risk of ketosis in dairy cattle. It was hypothesized that this ratio reflects the ability to break down blood phosphatidylcholine as a fatty acid resource. In the current study, 248 animals from a previous study were genotyped with Illumina BovineSNP50 BeadChip, and genome-wide association studies were carried out for the milk levels of phosphocholine, glycerophosphocholine, and the ratio of both metabolites. It was demonstrated that the latter two traits are heritable with h2 = 0.43 and h2 = 0.34, respectively. A major quantitative trait locus was identified on cattle chromosome 25. The APOBR gene, coding for the apolipoprotein B receptor, is located within this region and was analyzed as a candidate gene. The analysis revealed highly significant associations of polymorphisms within the gene with glycerophosphocholine as well as the metabolite ratio. These findings support the hypothesis that differences in the ability to take up blood phosphatidylcholine from low-density lipoproteins play an important role in early lactation metabolic stability of dairy cows and indicate APOBR to contain a causative variant.

Short communication: Use of genomic and metabolic information as well as milk performance records for prediction of subclinical ketosis risk via artificial neural networks

Subclinical ketosis is one of the most prevalent metabolic disorders in high-producing dairy cows during early lactation. This renders its early detection and prevention important for both economical and animal-welfare reasons. Construction of reliable predictive models is challenging, because traits like ketosis are commonly affected by multiple factors. In this context, machine learning methods offer great advantages because of their universal learning ability and flexibility in integrating various sorts of data. Here, an artificial-neural-network approach was applied to investigate the utility of metabolic, genetic, and milk performance data for the prediction of milk levels of β-hydroxybutyrate within and across consecutive weeks postpartum. Data were collected from 218 dairy cows during their first 5wk in milk. All animals were genotyped with a 50,000 SNP panel, and weekly information on the concentrations of the milk metabolites glycerophosphocholine and phosphocholine as well as milk composition data (milk yield, fat and protein percentage) was available. The concentration of β-hydroxybutyric acid in milk was used as target variable in all prediction models. Average correlations between observed and predicted target values up to 0.643 could be obtained, if milk metabolite and routine milk recording data were combined for prediction at the same day within weeks. Predictive performance of metabolic as well as milk performance-based models was higher than that of models based on genetic information.

Genomic additive and dominance variance of milk performance traits

Milk performance traits are likely influenced by both additive and non-additive (e.g. dominance) genetic effects. Genetic variation can be partitioned using genomic information. The objective of this study was to estimate genetic variance components of production and milk component traits (e.g. acetone, fatty acids), which are particularly important for milk processing or which can provide information on the health status of cows. A genomic relationship approach was applied to phenotypic and genetic information of 1295 Holstein cows for estimating additive genetic and dominance variance components. Most of the 17 investigated traits were mainly affected by additive genetic effects, but protein content and casein content also showed a significant contribution of dominance. The ratio of dominance to additive variance was estimated as 0.64 for protein content and 0.56 for casein content. This ratio was highest for SCS (1.36) although dominance was not significant. Dominance effects were negligible in other moderately heritable milk traits.

A field study to determine the prevalence, dairy herd management systems, and fresh cow clinical conditions associated with ketosis in western European dairy herds

The aim of this study was to determine the prevalence, major management systems, and fresh cow clinical conditions associated with ketosis in western European dairy herds. A total of 131 dairies were enrolled in Germany, France, Italy, the Netherlands, and the United Kingdom during 2011 to 2012. A milk-based test for ketones (Keto-Test; Sanwa Kagaku Kenkyusho Co. Ltd., Nagoya, Japan; distributed by Elanco Animal Health, Antwerp, Belgium) was used for screening cows between d 7 and 21 after calving and ketosis was defined as a Keto-Test ≥100µmol/L. Study cows were observed for clinical disease up to 35d postcalving. Multivariate analysis (generalized estimating equation logistic regression) was performed to determine country, farm, management, feed, and cow factors associated with ketosis and to determine associations between ketosis and fresh cow diseases. Thirty-nine percent of the cows were classified as having ketosis. The herd average of ketosis was 43% in Germany, 53% in France, 31% in Italy, 46% in the Netherlands, and 31% in the United Kingdom. Of the 131 farms, 112 (85%) had 25% or more of their fresh cows resulting as positive for ketosis. Clinical ketosis was not reported in most farms and the highest level of clinical ketosis reported was 23%. The risks of ketosis were significantly lower in Italy and the United Kingdom compared with France, the Netherlands, and Germany. Larger herd size was associated with a decreased risk of ketosis. The farms that fed partially mixed rations had 1.5 times higher odds of ketosis than those that fed total mixed rations. Cows that calved in April to June had the highest odds of ketosis, with about twice as high odds compared with cows that calved in July to September. The cows that calved in January to March tended to have 1.5 times higher risk of ketosis compared with cows that calved in July to September. The odds of ketosis in parity 2 and parity 3 to 7 was significantly higher (1.5 and 2.8 times higher, respectively) than the odds of ketosis in parity 1. The odds of ketosis was significantly smaller in parity 2 compared with parity 3 to 7. Ketosis was associated with significantly higher odds of all common fresh cow conditions: metritis, mastitis, displaced abomasum, clinical ketosis, lameness, and gastrointestinal disorders. Odds of ketosis in cows having had twins or dystocia were not increased, whereas higher odds of ketosis were observed in cows with milk fever or retained placenta.

Effects of time and sampling location on concentrations of β-hydroxybutyric acid in dairy cows

Two trials were conducted to examine factors potentially influencing the measurement of blood β-hydroxybutyric acid (BHBA) in dairy cows. The objective of the first trial was to study effects of sampling time on BHBA concentration in continuously fed dairy cows. Furthermore, we determined test characteristics of a single BHBA measurement at a random time of the day to diagnose subclinical ketosis considering commonly used cut-points (1.2 and 1.4 mmol/L). Finally, we set out to evaluate if test characteristics could be enhanced by repeating measurements after different time intervals. During 4 herd visits, a total of 128 cows (8 to 28 d in milk) fed 10 times daily were screened at 0900 h and preselected by BHBA concentration. Blood samples were drawn from the tail vessels and BHBA concentrations were measured using an electronic BHBA meter (Precision Xceed, Abbott Diabetes Care Ltd., Witney, UK). Cows with BHBA concentrations ≥0.8 mmol/L at this time were enrolled in the trial (n=92). Subsequent BHBA measurements took place every 3h for a total of 8 measurements during 24 h. The effect of sampling time on BHBA concentrations was tested in a repeated-measures ANOVA repeating sampling time. Sampling time did not affect BHBA concentrations in continuously fed dairy cows. Defining the average daily BHBA concentration calculated from the 8 measurements as the gold standard, a single measurement at a random time of the day to diagnose subclinical ketosis had a sensitivity of 0.90 or 0.89 at the 2 BHBA cut-points (1.2 and 1.4 mmol/L). Specificity was 0.88 or 0.90 using the same cut-points. Repeating measurements after different time intervals improved test characteristics only slightly. In the second experiment, we compared BHBA concentrations of samples drawn from 3 different blood sampling locations (tail vessels, jugular vein, and mammary vein) of 116 lactating dairy cows. Concentrations of BHBA differed in samples from the 3 sampling locations. Mean BHBA concentration was 0.3 mmol/L lower when measured in the mammary vein compared with the jugular vein and 0.4 mmol/L lower in the mammary vein compared with the tail vessels. We conclude that to measure BHBA, blood samples of continuously fed dairy cows can be drawn at any time of the day. A single measurement provides very good test characteristics for on-farm conditions. Blood samples for BHBA measurement should be drawn from the jugular vein or tail vessels; the mammary vein should not be used for this purpose.

Investigating associations between milk metabolite profiles and milk traits of Holstein cows

In the field of dairy cattle research, it is of great interest to improve the detection and prevention of diseases (e.g., mastitis and ketosis) and monitor specific traits related to the state of health and management. During the standard milk performance test, traditional milk traits are monitored, and quality and quantity are screened. In addition to the standard test, it is also now possible to analyze milk metabolites in a high-throughput manner and to consider them in connection with milk traits to identify functionally important metabolites that can also serve as biomarker candidates. We present a study in which 190 milk metabolites and 14 milk traits of 1,305 Holstein cows on 18 commercial farms were investigated to characterize interrelations of milk metabolites between each other, to milk traits from the milk standard performance test, and to influencing factors such as farm and sire effect (half-sib structure). The effect of influencing factors (e.g., farm) varied among metabolites and traditional milk traits. The investigations of associations between metabolites and milk traits revealed groups of metabolites that show, for example, positive correlations to protein and casein, and negative correlations to lactose and pH. On the other hand, groups of metabolites jointly associated with the investigated milk traits can be identified and functionally discussed. To enable a multivariate investigation, 2 machine learning methods were applied to detect important metabolites that are highly correlated with the investigated traditional milk traits. For somatic cell score, uracil, lactic acid, and 9 other important metabolites were detected. Lactic acid has already been proposed as a biomarker candidate for mastitis in the recent literature. In conclusion, we found sets of metabolites eligible to predict milk traits, enabling the analysis of milk traits from a metabolic perspective and discussion of the possible functional background for some of the detected associations.

Integrating milk metabolite profile information for the prediction of traditional milk traits based on SNP information for Holstein cows

In this study the benefit of metabolome level analysis for the prediction of genetic value of three traditional milk traits was investigated. Our proposed approach consists of three steps: First, milk metabolite profiles are used to predict three traditional milk traits of 1,305 Holstein cows. Two regression methods, both enabling variable selection, are applied to identify important milk metabolites in this step. Second, the prediction of these important milk metabolite from single nucleotide polymorphisms (SNPs) enables the detection of SNPs with significant genetic effects. Finally, these SNPs are used to predict milk traits. The observed precision of predicted genetic values was compared to the results observed for the classical genotype-phenotype prediction using all SNPs or a reduced SNP subset (reduced classical approach). To enable a comparison between SNP subsets, a special invariable evaluation design was implemented. SNPs close to or within known quantitative trait loci (QTL) were determined. This enabled us to determine if detected important SNP subsets were enriched in these regions. The results show that our approach can lead to genetic value prediction, but requires less than 1% of the total amount of (40,317) SNPs., significantly more important SNPs in known QTL regions were detected using our approach compared to the reduced classical approach. Concluding, our approach allows a deeper insight into the associations between the different levels of the genotype-phenotype map (genotype-metabolome, metabolome-phenotype, genotype-phenotype).

Elevated non-esterified fatty acids and β-hydroxybutyrate and their association with transition dairy cow performance

Dairy cows pass through a period of negative energy balance as they transition from late gestation to early lactation. Poor adaptation through this period, expressed as excessively elevated concentrations of non-esterified fatty acids (NEFAs) pre- or post-partum and elevated concentrations of β-hydroxybutyrate post-partum, increases an individual animal's risk of post-partum disease, removal from the herd, reproductive difficulty, and reduced milk production. Field studies have shown that subclinical ketosis often affects 40% of cows in a herd although the incidence can be as high as 80%. Peak incidence occurs at 5 days in milk, and cows that develop subclinical ketosis in the first week of lactation have a higher risk of negative effects and reduced milk production than cows that develop subclinical ketosis in the second week of lactation. Herds with more than a 15-20% prevalence of excessively elevated concentrations of NEFAs and β-hydroxybutyrate in early lactation have higher rates of negative subsequent events, poorer reproduction, and lower milk yield than herds with a lower prevalence of negative energy balance. This paper reviews (1) strategies for testing of energy-related metabolites, (2) consequences of poor adaptation to negative energy balance (for individual animals and for herds), (3) treatment approaches for affected cows, and (4) economic considerations for testing and treating cows with poor adaptation to negative energy balance.

Milk metabolites and their genetic variability

The composition of milk is crucial to evaluate milk performance and quality measures. Milk components partly contribute to breeding scores, and they can be assessed to judge metabolic and energy status of the cow as well as to serve as predictive markers for diseases. In addition to the milk composition measures (e.g., fat, protein, lactose) traditionally recorded during milk performance test via infrared spectroscopy, novel techniques, such as gas chromatography-mass spectrometry, allow for a further analysis of milk into its metabolic components. Gas chromatography-mass spectrometry is suitable for measuring several hundred metabolites with high throughput, and thus it is applicable to study sources of genetic and nongenetic variation of milk metabolites in dairy cows. Heritability and mode of inheritance of metabolite measurements were studied in a linear mixed model approach including expected (pedigree) and realized (genomic) relationship between animals. The genetic variability of 190 milk metabolite intensities was analyzed from 1,295 cows held on 18 farms in Mecklenburg-Western Pomerania, Germany. Besides extensive pedigree information, genotypic data comprising 37,180 single nucleotide polymorphism markers were available. Goodness of fit and significance of genetic variance components based on likelihood ratio tests were investigated with a full model, including marker- and pedigree-based genetic effects. Broad-sense heritability varied from zero to 0.699, with a median of 0.125. Significant additive genetic variance was observed for highly heritable metabolites, but dominance variance was not significantly present. As some metabolites are particularly favorable for human nutrition, for instance, future research should address the identification of locus-specific genetic effects and investigate metabolites as the molecular basis of traditional milk performance test traits.

Routine detection of hyperketonemia in dairy cows using Fourier transform infrared spectroscopy analysis of β-hydroxybutyrate and acetone in milk in combination with test-day information

The objective of this study was to assess the quality of a diagnostic model for the detection of hyperketonemia in early lactation dairy cows at test days. This diagnostic model comprised acetone and β-hydroxybutyrate (BHBA) concentrations in milk, as determined by Fourier transform infrared (FTIR) spectroscopy, in addition to other available test-day information. Plasma BHBA concentration was determined at a regular test day in 1,678 cows between 5 and 60 d in milk, originating from 118 randomly selected farms in the Netherlands. The observed prevalence of hyperketonemia (defined as plasma BHBA ≥1,200 µmol/L) was 11.2%. The value of FTIR predictions of milk acetone and milk BHBA concentrations as single tests for hyperketonemia were found limited, given the relatively large number of false positive test-day results. Therefore, a multivariate logistic regression model with a random herd effect was constructed, using parity, season, milk fat-to-protein ratio, and FTIR predictions of milk acetone and milk BHBA as predictive variables. This diagnostic model had 82.4% sensitivity and 83.8% specificity at the optimal cutoff value (defined as maximum sum of sensitivity and specificity) for the detection of hyperketonemia at test days. Increasing the cutoff value of the model to obtain a specificity of 95% increased the predicted value of a positive test result to 56.5%. Confirmation of test-positive samples with wet chemistry analysis of milk acetone or milk BHBA concentrations (serial testing) improved the diagnostic performance of the test procedure. The presented model was considered not suitable for individual detection of cows with ketosis due to the length of the test-day interval and the low positive predictive values of the investigated test procedures. The diagnostic model is, in our opinion, valuable for herd-level monitoring of hyperketonemia, especially when the model is combined with wet chemistry analysis of milk acetone or milk BHBA concentrations. By using the diagnostic model in combination with wet chemistry milk BHBA analysis, 84% of herds were correctly classified at a 10% alarm-level prevalence. As misclassification of herds may particularly occur when only a limited number of fresh cows are sampled, we suggest using prevalence estimates over several consecutive test days to evaluate feeding and management practices in smaller dairy farms.

Effects of dietary carbohydrates on rumen epithelial metabolism of nonlactating heifers

Ruminal wall metabolism was studied in nonlactating heifers by altering the carbohydrate (CHO) digestion site between rumen and intestine. The CHO digestion site was estimated from in situ and total-tract digestibility of control (CONT) diets and diets supplemented with corn (CRN), barley (BARL), or soy hulls (SOYH). Ruminal epithelial metabolism regulating gene expression, morphology, and nutrient delivery was assessed from a combination of rumen volatile fatty acid (VFA) concentration, biopsies for papilla morphology, and expression of putative metabolic regulatory genes encoding enzymes that facilitate VFA utilization. Digestible dry matter and CHO intake were 25 and 45% higher, respectively, in the supplemented diets than in CONT diets. Fiber supplementation increased the intestinal and decreased ruminal CHO digestion. Ruminal nonfiber CHO digestibility was 10% lower in CRN than with the high rumen-degradable supplement. The CONT heifers had lowest total ruminal VFA and highest acetate concentration relative to the other treatments. Total VFA concentration in BARL and CRN diets tended to be higher than in SOYH. The SOYH diet tended to reduce papilla dimension relative to CRN and BARL. The CRN diet tended to increase papilla surface area relative to BARL and SOYH. Gene expression of propionyl-coenzyme A carboxylase was higher in CRN and BARL than in SOYH diets, and tended to be higher in CRN than in BARL and SOYH diets. Lactate dehydrogenase and butyryl coenzyme A synthase gene transcripts tended to be higher in CONT than in the supplemented treatments. Thus, rumen epithelial expression of genes involved in VFA metabolism and ruminal wall-structure development are influenced by other regulatory mechanism that is not directly affected by local signals. The in situ methods used are a useful tool for differentiating ruminal from extraruminal nutrient supply.

Alterations of fatty acid β-oxidation capability in the liver of ketotic cows

Dairy cows are highly susceptible to ketosis after parturition. In the present study, we evaluated the expression of fatty acid β-oxidation-related enzymes in the liver of ketotic (n=6) and nonketotic (n=6) cows. Serum levels of nonesterified fatty acids (NEFA), β-hydroxybutyrate (BHBA), and glucose were determined by using standard biochemical techniques. The mRNA abundance and protein content of acyl-CoA synthetase long-chain (ACSL), carnitine palmitoyltransferase I (CPT I), carnitine palmitoyltransferase II (CPT II), acyl-CoA dehydrogenase long chain (ACADL), 3-hydroxy-3-methylglutaryl-CoA synthase (HMGCS), and acetyl-CoA carboxylase (ACC) were evaluated by real-time PCR and ELISA. We found that serum glucose levels were lower in ketotic cows than in nonketotic cows, but serum BHBA and NEFA concentrations were higher. Messenger RNA and protein levels of ACSL were significantly higher in livers of ketotic cows than those in nonketotic cows. In contrast, mRNA levels of CPT I and mRNA and protein levels of CPT II, ACADL, HMGCS, and ACC were decreased in the liver of ketotic cows. Serum NEFA concentration positively correlated with ACSL protein levels and negatively correlated with protein levels of CPT II, HMGCS, ACADL, and ACC. In addition, serum BHBA concentration negatively correlated with protein levels of CPT II, HMGCS, and ACADL. Overall, fatty acid β-oxidation capability was altered in the liver of ketotic compared with nonketotic cows. Furthermore, high serum NEFA and BHBA concentrations play key roles in affecting pathways of fatty acid metabolism in the liver.

NMR metabolomic analysis of dairy cows reveals milk glycerophosphocholine to phosphocholine ratio as prognostic biomarker for risk of ketosis

Ketosis is a common metabolic disease in dairy cows. Diagnostic markers for ketosis such as acetone and beta-hydroxybutyric acid (BHBA) are known, but disease prediction remains an unsolved challenge. Milk is a steadily available biofluid and routinely collected on a daily basis. This high availability makes milk superior to blood or urine samples for diagnostic purposes. In this contribution, we show that high milk glycerophosphocholine (GPC) levels and high ratios of GPC to phosphocholine (PC) allow for the reliable selection of healthy and metabolically stable cows for breeding purposes. Throughout lactation, high GPC values are connected with a low ketosis incidence. During the first month of lactation, molar GPC/PC ratios equal or greater than 2.5 indicate a very low risk for developing ketosis. This threshold was validated for different breeds (Holstein-Friesian, Brown Swiss, and Simmental Fleckvieh) and for animals in different lactations, with observed odds ratios between 1.5 and 2.38. In contrast to acetone and BHBA, these measures are independent of the acute disease status. A possible explanation for the predictive effect is that GPC and PC are measures for the ability to break down phospholipids as a fatty acid source to meet the enhanced energy requirements of early lactation.

Latent class evaluation of a milk test, a urine test, and the fat-to-protein percentage ratio in milk to diagnose ketosis in dairy cows

In this study, 3 commonly used tests to diagnose ketosis were evaluated with a latent class model to avoid the assumption of an available perfect test. The 3 tests were the KetoLac BHB (Sanwa Kagaku Kenkyusho Co. Ltd., Nagoya, Japan) test strip that tests milk for β-hydroxybutyrate, the KetoStix (Bayer Diagnostics Europe Ltd., Dublin, Ireland) test strip that tests urine for acetoacetate, and the fat-to-protein percentage ratio (FPR) in milk. A total of 8,902 cows were included in the analysis. The cows were considered to be a random sample from the population of Danish dairy cattle under intensive management, thus representing a natural spectrum of ketosis as a disease. All cows had a recorded FPR between 7 and 21 d postpartum. The KetoLac BHB recordings were available from 2,257 cows and 6,645 cows had a KetoStix recording. The recordings were analyzed with a modified Hui-Walter model, in a Bayesian framework. The specificity of the KetoLac BHB test and the KetoStix test were both high [0.99 (0.97-0.99)], whereas the specificity of FPR was somewhat lower [0.79 (0.77-0.81)]. The best sensitivity was for the KetoStix test [0.78 (0.55-0.98)], followed by the FPR [0.63 (0.58-0.71)] and KetoLac BHB test [0.58 (0.35-0.93)].

Prevalence of subclinical ketosis in dairy cattle in the Southwestern Iran and detection of cutoff point for NEFA and glucose concentrations for diagnosis of subclinical ketosis

Subclinical ketosis (SCK) is simply a condition marked by increased levels of circulating ketone bodies without the presence of the clinical signs of ketosis. Subclinical ketosis can cause economic losses through decreased milk production and association with preparturient diseases. Limited information is available regarding the prevalence of SCK in dairy herds in Southwestern Iran. The objectives of this study were (i) determination of the cutoff point of nonesterified fatty acids (NEFAs) and glucose concentrations for diagnosis of SCK using receiver operating characteristic (ROC) analysis, and (ii) determination of prevalence of subclinical ketosis in apparently healthy dairy cattle in Southwestern Iran. From October to December 2009, a total of 100 clinically healthy multiparous Holstein cows (3-8 years old) were randomly selected from 16 dairy herds around Kazerun, Fars Province, Iran. The cows had two-six lactations, with body weight ranging from 500 to 650 kg. Blood samples for each cow were taken at 2, 4 and 6 weeks post parturition and 3-4h after the morning feeding. The optimal cutoff point was set, by the ROC method, to >0.26 mmol/L for NEFA, and < 2.26 mmol/L for glucose with corresponding 82.54% sensitivity and 91.89% specificity for NEFA and 44.44% sensitivity and 78.38% specificity for glucose. Cows with BHB concentrations higher than 1200 μmol/L were classified as having SCK. In 2, 4 and 6 weeks post parturition 63%, 68% and 59% of the tested cows were subclinically ketotic. Overall, 97% of tested cows (97/100) were considered subclinically ketotic in at least one sample period. Thirty percent of tested cows (30/100) suffered from subclinical ketosis in all of the 2, 4 and 6 weeks postpartum. The results suggest that, a cut-off point of 0.26 mmol/L for NEFA concentrations can be used during early lactation for diagnosis of subclinical ketosis and making management decisions for prevention and treatment. Glucose cannot be a good criterion for diagnosis of SCK and it does not appear to be useful for monitoring subclinical ketosis.