Subclinical Ketosis In Dairy Cows Research Articles

Hemato-biochemical and Ultrasonographic Studies on Subclinical Ketosis in Dairy Cows

Hemato-biochemical and Ultrasonographic Studies on Subclinical Ketosis in Dairy Cows

Incidência de cetose subclínica em vacas leiteiras na região semiárida da Paraíba

Subclinical ketosis (SCK) is one of the main metabolic diseases to occur during the transition period in dairy herds. The aim of this study was to evaluate the localized incidence of SCK in dairy cows in the semi-arid region of the state of Paraíba, Brazil, and to correlate this data with the number of weeks of lactation and the body condition score (BCS). Samples were collected from the coccygeal vein of 257 lactating dairy cows, with a view to determining serum ketone and glucose body levels. Animals that presented serum ketone body levels equal to or greater than 1.2 mmol/dL were considered to have SCK. It was found that 5.45% of the dairy cows presented SCK. The weeks of lactation and the BCS had no influence (P > 0.05) on the levels of serum ketone bodies or glucose of the dairy cows. For serum ketone body levels, 94.55% (243) of the cows remained within the normal limits for metabolites. Most of the cows, 67.70% (174) were within normal serum glucose levels, while 23.35% (60) presented hypoglycaemia, and 8.95% (23) presented hyperglycaemia. The BCS and serum glucose levels are negatively correlated, with a coefficient of -0.1329 (P = 0.0335). No significance was found 0.087 (P = 0.1608) for the correlation between the BCS and serum ketone body levels. The localized incidence of SCK in this study was 5.45% of the total number of cows, with the BCS and number of weeks of lactation having no influence on incidence of the disorder.

The rumen microbiota contributed to the development of mastitis induced by subclinical ketosis

BackgroundMastitis is a serious disease which affects animal husbandry, particularly in cow breeding. The etiology of mastitis is complex and its pathological mechanism is not yet fully understood. Our previous research in clinical investigation has revealed that subclinical ketosis can increase the number of somatic cell counts (SCC) in milk, although the underlying mechanism remains unclear. Recent studies have further confirmed the significant role of mastitis. ResultsIn this study, we aimed to examine the SCC, rumen microbiota, and metabolites in the milkmen of cows with subclinical ketosis. Additionally, we conducted a rumen microbiota transplant into mice to investigate the potential association between rumen microbiota disturbance and mastitis induced by subclinical ketosis in dairy cows. The study has found that cows with subclinical ketosis have a higher SCC in their milk compared to healthy cows. Additionally, there were significant differences in the rumen microbiota and the level of volatile fatty acid (VFA) between cows with subclinical ketosis and healthy cows. Moreover, transplanting the rumen microbiota from subclinical ketosis and mastitis cows into mice can induce mammary inflammation and liver function damage than transplanting the rumen flora from healthy dairy cows. ConclusionsIn addition to the infection of mammary gland by pathogenic microorganisms, there is also an endogenous therapeutic pathway mediated by rumen microbiota. Targeted rumen microbiota modulation may be an effective way to prevent and control mastitis in dairy cows.

Rumination Assessment: A Managemental Tool for Dairy Cattle

Rumination is the process of regurgitation, re-mastication, salivation and swallowing of ingesta. Cows ruminate for approximately 504±93 min/day and a decrease in rumination time is typically important sign that something is affecting ruminal function and cow well-being. Rumination activity mainly occurs at night and during resting periods in the afternoon. Rumination can be detected by the several methods viz. visual observation, video recording, nose band pressure signal, accelerometer ear-tags and neck collar. The time that an animal spends ruminating is influenced by species, breed, physical & chemical characteristics of the diet, health condition, feed intake and production level. More roughage diet with long fibre increases the rumination time. Diets containing higher neutral detergent fibre (%) resulted in increase in rumination time per kilogram of roughage ingested. Rumination time decrease with increasing temperature humidity index. Rumination time significantly influence the milk yield and milk composition and reproduction performance. Rumination time could be used to monitor dairy cattle during calving, oestrous, and heat stress, as well as to detect diseases, heat stress, transition cow disorders and subclinical ketosis in dairy cows.

Time spent feeding as an early indicator of metritis in postpartum dairy cows: systematic review and meta-analysis

Context Feeding behaviour is an important tool for the early detection of diseases in dairy cows. Aims The aim of this study was to evaluate whether the variation in time spent feeding in the prepartum and postpartum periods may be used to detect the occurrence of metritis and subclinical ketosis (SCK) before the onset of the clinical symptoms at the postpartum. Methods The research was conducted in four electronic databases, including Scopus, Science Direct, Pubmed, and Web of Science. The inclusion criteria for citations were original research, evaluation of daily time spent feeding in dairy cows, and use of this indicator for early identification of metritis and/or SCK in dairy cows in the prepartum and postpartum periods. A random-effect meta-analysis (MA) was conducted for metritis with the time spent feeding means of control (healthy) and treated (sick) groups measured in the prepartum and postpartum periods. The analysis was conducted with the values of daily time spent feeding before and after calving in both groups. Key results In total, 26 trials from six papers, involving 1037 dairy cows, were included in the statistical analysis. No data were obtained for SCK to conduct a MA, while for metritis, 16 trials from six papers (prepartum) and 10 trials from three papers (postpartum) were considered. The heterogeneity between studies on metritis was moderate (I2 = 57.5%) in the prepartum period and low (I2 = 10.0%) in the postpartum period. The mean difference feeding time for healthy and unhealthy animals was greater during postpartum (21.14 min/day, P < 0.001) than during prepartum (16.36 min/day, P < 0.001). Meta-regression analysis showed that number of daily milkings, sample size, and the place of running trial significantly influenced the time spent feeding by cows. Conclusion On the basis of available and suitable scientific literature, time spent feeding is reduced at the prepartum and postpartum periods in dairy cows further affected by metritis. Implications This study evidenced that feeding time might be incorporated into health-monitoring protocols for early detection of metritis in dairy cows.

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

Simple SummaryKetosis is a serious metabolic disease in high-yield dairy cows, that affects productive herds throughout the world. Subclinical ketosis is one of the most dominant metabolic disorders in dairy herds during early lactation, so early detection and prevention are important for both economic and animal welfare reasons. Neural networks, which offer a high degree of accuracy in predicting various phenomena and processes where there is no clear causal correlation or there are no rules that allow the establishment of a logical cause-and-effect relationship, can be used to address problems related to prediction, classification, or control. A Multi-Layer perceptron (MLP) is a feedforward artificial neural network model that takes input data for a set of proper output. This study investigated the performance of four algorithms used to train MLP networks. The experimental results demonstrate that the MLP network model improved the accuracy of process recognition of subclinical ketosis in dairy cows. The received artificial model’s results were saved in the predictive model markup language (PMML) and can be used to describe the learning set, the algorithm used in the data mining application and related information.Subclinical ketosis is one of the most dominant metabolic disorders in dairy herds during lactation. Cows suffering from ketosis experience elevated ketone body levels in blood and milk, including β-hydroxybutyric acid (BHB), acetone (ACE) and acetoacetic acid. Ketosis causes serious financial losses to dairy cattle breeders and milk producers due to the costs of diagnosis and management as well as animal welfare reasons. Recent years have seen a growing interest in the use of artificial neural networks (ANNs) in various fields of science. ANNs offer a modeling method that enables the mapping of highly complex functional relationships. The purpose of this study was to determine the relationship between milk composition and blood BHB levels associated with subclinical ketosis in dairy cows, using feedforward multilayer perceptron (MLP) artificial neural networks. The results were verified based on the estimated sensitivity and specificity of selected network models, an optimum cut-off point was identified for the receiver operating characteristic (ROC) curve and the area under the ROC curve (AUC). The study demonstrated that BHB, ACE and lactose (LAC) levels, as well as the fat-to-protein ratio in milk, were important input variables in the network training process. For the identification of cows at risk of subclinical ketosis, variables such as BHB and ACE levels in milk were of particular relevance, with a sensitivity and specificity of 0.84 and 0.61, respectively. It was found that the back propagation algorithm offers opportunities to integrate artificial intelligence and dairy cattle welfare within a computerized decision support tool.

Prevalence, risk factors and metabolic predictors associated with subclinical ketosis on dairy cattle in Batna, Eastern of Algeria

Economic impact of subclinical ketosis (SCK) in dairy herds resulted from a decreased productivity, reproductive performances, and health condition. In Algeria, data availability regarding the prevalence of SCK in dairy cows were limited. The present study is a contribution in that direction and aimed to determine the prevalence of SCK between 2 and 50 days postpartum whereas investigating herd- and cow-level risk factors and the metabolic profile associated, on two bovine populations imported and native. The study was conducted with 16 typical representative local dairy cow herds (n = 100; 2–12 years old; located at Merouana, Batna Province) with data collected in a period comprised between August and February of the following year. Concentration of cows’ plasma beta-hydroxybutyrate (BHBA) was used for the diagnosis, with values equal or higher than 1.2 mmol/L chosen as the threshold for SCK detection. Other key associated zootechnical parameters such as body condition score (BCS) were monitored at the same sampling points. Logistic regression analyses were carried out to characterize association between parameters. The average SCK prevalence was 9% and resulted highest during first- and second-week postcalving, with a peak in the first week. The herd size (< 30 lactating cows) and BCS (≤ 3) were risk factors associated with SCK prevalence. At the cow-level, risks of SCK increased in thin individuals (P = 0.058; odds ratio (OR) = 5.00, 95% confidence interval (CI) = 0.95–26.32). At the herd-level, risks of SCK increased in smaller frame size herds (P = 0.047; OR = 8.76, 95% CI = 1.03–74.49). Most cases of SCK were observed in native cows compared to imported. Interestingly, linear stepwise regression showed a good relationship between BHBA and total bilirubin (P = 0.0001; R2 = 0.59). There were significant differences between healthy and ketotic cows regarding plasma concentration of bilirubin (P = 0.003), glucose (P = 0.001), alanine aminotransferase (ALT) (P = 0.008), creatinine (P = 0.007), and sodium (P = 0.02). According to receiver operating characteristic (ROC) curve analysis, the best parameter for SCK prediction is total bilirubin with more than 4.09 g/L as a cutoff point (77.78% sensitivity and 94.51% specificity). According to metabolic profile and feeding data we suggest that most cases of SCK observed in our study are type I. Further studies are required to validate other metabolic predictors for SCK and investigate adaptation of imported cows in semiarid regions in Algeria.

Global prevalence of subclinical ketosis in dairy cows: A systematic review and meta-analysis

The objectives of this study were to estimate the global prevalence of SCK in dairy cows, to identify optimum thresholds for BHB determining, to explore heterogeneity for some particular characteristics (continents, diagnosis techniques, cut-off, sample type, parity, breed, and housing), and to determine the SCK prevalence trend. SCK prevalence levels in dairy cows from various countries were calculated using bibliographic databases up until May 17, 2021 to screen titles and abstracts for possible eligibility. Thirty-eight studies from six continents were then included using a random-effects model to calculate the pooled SCK prevalence. Subgroup meta-analyses were performed to assess the heterogeneity of the prevalence for the above characteristics. Meta-regression and cumulative meta-analysis were used to assess the SCK prevalence trends. The results showed that the global prevalence of SCK in dairy cows was 22.7% [95% CI 21.2–24.3%]. No significant differences in SCK prevalence were observed among continents, diagnostic techniques, cut-off values—≥1.0, ≥1.2 and ≥1.4 mmol/L, sample types—milk and blood, and parities. However, the prevalence in Holsteins (19.8%) was significantly lower than other mixed breeds (23.7%). The SCK prevalence was significantly higher in indoor barns (27.8%) than in pasture and unspecified housing. In meta-regression, the SCK prevalence was not associated with study years or days in milk. The cumulative evidence suggested that the prevalence of SCK in dairy cows was associated with a wide range of risk factors. This study illustrates that the global prevalence of SCK in dairy cows is quite high; therefore, there is an urgent need to globally reduce the SCK prevalence in dairy farms.

Dietary supplementation with rumen-protected capsicum during the transition period improves the metabolic status of dairy cows

In ruminants, it has been observed that capsicum oleoresin can alter insulin responses and that high-intensity artificial sweetener can increase glucose absorption from the small intestine. Because glucose metabolism and insulin responses are critical during early lactation, these supplements might have an effect on the metabolic status of dairy cows during the transition period. The objective of this experiment was to evaluate the effects of rumen-protected capsicum oleoresin fed alone or in combination with artificial sweetener during the transition period on lactational performance and susceptibility to subclinical ketosis in dairy cows. Fifteen primiparous and 30 multiparous Holstein cows (a total of 39 cows finished the study) were arranged in a randomized complete block design during d -21 to 60 relative to parturition. Cows within block were randomly assigned to one of the following treatments: no supplement (CON), supplementation with 100 mg of rumen-protected capsicum/cow per day (RPCap), or RPCap plus 2 g of high-intensity artificial sweetener/cow per day (RPCapS). For both the RPCap and RPCapS treatments, only rumen-protected capsicum was fed during the dry period. From d 8 to 11 of lactation, intake was limited to 70% of predicted dry matter intake to induce subclinical ketosis. Production variables were recorded daily, samples for milk composition were collected on wk 2, 4, 6, and 8, and blood samples were collected on wk -2, 1, 2, and 4 of the experiment for analysis of metabolic hormones and blood cell counts. Supplementation with rumen-protected capsicum increased serum insulin and decreased β-hydroxybutyrate concentrations precalving, indicating a decrease in lipolysis. During the lactation period, RPCap was associated with a trend for increased milk production and feed efficiency following the ketosis challenge. Supplementation with RPCapS appeared to negate the response to rumen-protected capsicum. All cows developed subclinical ketosis during the challenge, and this was not affected by treatment. We conclude that treatments did not decrease susceptibility to subclinical ketosis; however, dietary supplementation with rumen-protected capsicum was effective at improving energy status precalving and tended to increased milk production and feed efficiency. The mechanism underlying these responses is unclear.

Predicting Subclinical Ketosis in Dairy Cows Using Machine Learning Techniques

Simple SummaryThe maintenance of cows in good health and physical condition is an important component of dairy cattle management. One of the major metabolic disorders in dairy cows is subclinical ketosis. Due to financial and organizational reasons it is often impossible to test all cows in a herd for ketosis using standard blood examination method. Using milk data from test-day records, obtained without additional costs for breeders, we found diagnostic models identifying cows-at-risk of subclinical ketosis. In addition, to select the best models, we present a general scoring approach for various machine learning models. With our models, breeders can identify dairy cows-at-risk of subclinical ketosis and implement appropriate management strategies and prevent losses in milk production.The diagnosis of subclinical ketosis in dairy cows based on blood ketone bodies is a challenging and costly procedure. Scientists are searching for tools based on results of milk performance assessment that would allow monitoring the risk of subclinical ketosis. The objective of the study was (1) to design a scoring system that would allow choosing the best machine learning models for the identification of cows-at-risk of subclinical ketosis, (2) to select the best performing models, and (3) to validate them using a testing dataset containing unseen data. The scoring system was developed using two machine learning modeling pipelines, one for regression and one for classification. As part of the system, different feature selections, outlier detection, data scaling and oversampling methods were used. Various linear and non-linear models were fit using training datasets and evaluated on holdout, testing the datasets. For the assessment of suitability of individual models for predicting subclinical ketosis, three β-hydroxybutyrate concentration in blood (bBHB) thresholds were defined: 1.0, 1.2 and 1.4 mmol/L. Considering the thresholds of 1.2 and 1.4, the logistic regression model was found to be the best fitted model, which included independent variables such as fat-to-protein ratio, acetone and β-hydroxybutyrate concentrations in milk, lactose percentage, lactation number and days in milk. In the cross-validation, this model showed an average sensitivity of 0.74 or 0.75 and specificity of 0.76 or 0.78, at the pre-defined bBHB threshold 1.2 or 1.4 mmol/L, respectively. The values of these metrics were also similar in the external validation on the testing dataset (0.72 or 0.74 for sensitivity and 0.80 or 0.81 for specificity). For the bBHB threshold at 1.0 mmol/L, the best classification model was the model based on the SVC (Support Vector Classification) machine learning method, for which the sensitivity in the cross-validation was 0.74 and the specificity was 0.73. These metrics had lower values for the testing dataset (0.57 and 0.72 respectively). Regression models were characterized by poor fitness to data (R2 < 0.4). The study results suggest that the prediction of subclinical ketosis based on data from test-day records using classification methods and machine learning algorithms can be a useful tool for monitoring the incidence of this metabolic disorder in dairy cattle herds.

Rumination time as an early predictor of metritis and subclinical ketosis in dairy cows at the beginning of lactation: Systematic review-meta-analysis

Daily rumination time (RT; min/d) is recognized as an important tool for assessing the health of dairy cows, which may depend on the disease, lactation stage and individual cows. Using a systematic review-meta-analysis, this study evaluated whether the variation in RT is effective for early detection of metritis and subclinical ketosis (SCK) in dairy cows in the pre and post-partum periods (from three weeks before to three weeks after calving). The research was carried out in four electronic databases - Scopus, Science Direct, Pubmed and Web of Science. The main inclusion criteria were original research; evaluation of RT in dairy cows; and use of RT for early identification of metritis and/or SCK in post-partum dairy cows. A random effect meta-analysis (MA) was conducted for each disease (metritis and SCK) separately, with the RT means of healthy and sick groups, measured in the pre and post-partum. The effect size used was the mean difference (MD).Twenty-two trials from six studies were included in the MA, involving 1494 dairy cows. For metritis, four trials from three studies in the pre-partum period were considered as well as five trials from four studies in the post-partum. For SCK, six trials from four studies pre-partum and seven trials from five studies in the post-partum period were taken into consideration. The heterogeneity between studies for metritis was null (I2 = 0%) and low (I2 = 5.7 %) in the pre-partum and in the post-partum, respectively. The MD of RT between healthy cows and those with metritis was different in the pre (MD =0.411 min/d; P < 0.001) and in the post-partum (MD =0.279 min/d; P < 0.001). In SCK, heterogeneity was high in the pre (I2 = 69 %) and in the post-partum (I2 = 58.1 %), and the MD of RT was similar between healthy and sick cows (P> 0.05). In a meta-regression, RT from primiparous cows showed a lower predicted value for MD (0.48 min. d; P < 0.05) compared to multiparous cows, and the increment in each unit of milk production decreased the predicted MD value by 0.08 min. d (P < 0.001). Our MA demonstrates that RT is a good predictor for early detection of metritis in pre and post-partum; however, it is not an adequate predictor for SCK. Further investigations using more frequent blood sampling and the same threshold values for BHB are required to assess the adequacy of rumination time to predict SCK.

Addition of straw to the early-lactation diet: Effects on feed intake, milk yield, and subclinical ketosis in Holstein cows

This study evaluated feed intake, milk yield, and subclinical ketosis in dairy cows in early lactation fed 2 different diets postpartum. Cows are typically offered a high-energy ration immediately after calving. We compared a conventional high-energy total mixed ration (TMR) with a transition ration that contained chopped straw. We predicted that adding chopped straw would increase dry matter intake, milk production, and indicators of energy metabolism during the first 3 wk of lactation compared to cows fed a conventional high-energy TMR. We also predicted that carryover effects would be likely for at least 2 wk after treatment ended. A total of 68 mixed-age Holstein cows were enrolled in the study 3 wk before their expected calving. All cows were managed on a single high-forage diet during the dry period. At calving, cows were allocated to 1 of the 2 diets: half to the conventional high-energy TMR (CTMR; n = 34; net energy for lactation = 1.61 Mcal/kg; neutral detergent fiber = 31.7%), and the other half to a high-forage TMR containing chopped wheat straw, equivalent to 4.27% dry matter (STMR; n = 34; net energy for lactation = 1.59 Mcal/kg; neutral detergent fiber = 33.7%) for 3 wk after calving. Cows on STMR were then shifted to CTMR for the next 2 wk to study short-term residual effects on the performance of cows. Treatments were balanced for parity, body condition score, and body weight. Feed intake was measured daily from 2 wk before to 5 wk after calving using automatic feed bins. Blood was sampled twice weekly from 2 wk before to 5 wk after calving, and β-hydroxybutyrate and glucose were measured in serum samples. Subclinical ketosis was identified using a threshold of β-hydroxybutyrate ≥1.0 mmol/L in wk 1 after calving and ≥1.2 mmol/L in wk 2 to 5 after calving. Cows were milked twice daily, and weekly samples (composite samples of morning and afternoon milkings) were analyzed to determine total solids, fat, protein, lactose, and somatic cell count. Data were analyzed in 2 separate periods: the treatment phase (wk +1, +2, and +3) and the post-treatment phase (wk +4 and +5). The addition of straw to the TMR negatively affected the dry matter intake of STMR cows during wk 2 and 3 of lactation. Daily milk yield during the first 5 wk of lactation was lower in STMR cows than in CTMR cows. Concentrations of β-hydroxybutyrate were higher in CTMR cows than in STMR cows during wk 1, but this effect was reversed during wk 2 and 3 of lactation. By 21 d in milk, STMR cows had a greater risk of developing subclinical ketosis than CTMR cows. Adding chopped wheat straw to the TMR during the first 21 d after calving lowered dry matter intake and provided no metabolic or production benefits to lactating dairy cattle.

Expert opinion as priors for random effects in Bayesian prediction models: Subclinical ketosis in dairy cows as an example.

Random effects regression models are routinely used for clustered data in etiological and intervention research. However, in prediction models, the random effects are either neglected or conventionally substituted with zero for new clusters after model development. In this study, we applied a Bayesian prediction modelling method to the subclinical ketosis data previously collected by Van der Drift et al. (2012). Using a dataset of 118 randomly selected Dutch dairy farms participating in a regular milk recording system, the authors proposed a prediction model with milk measures as well as available test-day information as predictors for the diagnosis of subclinical ketosis in dairy cows. While their original model included random effects to correct for the clustering, the random effect term was removed for their final prediction model. With the Bayesian prediction modelling approach, we first used non-informative priors for the random effects for model development as well as for prediction. This approach was evaluated by comparing it to the original frequentist model. In addition, herd level expert opinion was elicited from a bovine health specialist using three different scales of precision and incorporated in the prediction as informative priors for the random effects, resulting in three more Bayesian prediction models. Results showed that the Bayesian approach could naturally take the clustering structure of clusters into account by keeping the random effects in the prediction model. Expert opinion could be explicitly combined with individual level data for prediction. However in this dataset, when elicited expert opinion was incorporated, little improvement was seen at the individual level as well as at the herd level. When the prediction models were applied to the 118 herds, at the individual cow level, with the original frequentist approach we obtained a sensitivity of 82.4% and a specificity of 83.8% at the optimal cutoff, while with the three Bayesian models with elicited expert opinion, we obtained sensitivities ranged from 78.7% to 84.6% and specificities ranged from 75.0% to 83.6%. At the herd level, 30 out of 118 within herd prevalences were correctly predicted by the original frequentist approach, and 31 to 44 herds were correctly predicted by the three Bayesian models with elicited expert opinion. Further investigation in expert opinion and distributional assumption for the random effects was carried out and discussed.

Influence of Subclinical Ketosis in Dairy Cows on Ingestive-Related Behaviours Registered with a Real-Time System.

Simple SummaryEarlier disease detection may benefit cows by improving responses to treatment. We expected that changes in rumination before evident clinical signs of subclinical ketosis would result in the earlier identification of disease. Cows with subclinical ketosis showed lesser average values for the following parameters: rumination time and rumination chews (1.48 and 1.68 times respectively; p < 0.001), drinking time (1.50 times; p < 0.001), chews per minute, bolus and chews per bolus (1.12, 1.45 and 1.51 times; p < 0.001). From the 15th day before the diagnosis of ketosis, rumination time in the healthy group was greater than that in subclinical ketosis cows from −0.96% (−17 day) to 187.79% (0 days, p < 0.001). Eating time at the beginning of the experiment in healthy cows was 48.92% and at the end of the period was −91.97% lesser compared to subclinical ketosis (p < 0.001).According to the literature, rumination time can be used as biomarker in the diagnosis of subclinical ketosis (SCK). We hypothesized that SCK in cows influences ingestive-related behaviours registered with the real-time system. The aim of the current study was to determine the influence of SCK on dairy cows’ ingestive-related behaviours registered with a real-time system. Twenty Lithuanian Black and White breed dairy cows were selected based on the following criteria: First day after calving, having two or more lactations (on average 3.0 ± 0.13 lactations), and being clinically healthy. The experiment lasted 18 days. Cows were tested 24 h a day for 17.5 days. On the day of diagnosis (day 0), data were recorded for 12 h. During the experimental period, one cow was studied for a total of 420 h. For the registration of rumination behaviour, the RumiWatch system (RWS) was used. It was found that cows with SCK showed lesser average values for the following parameters: rumination time and rumination chews (1.48 and 1.68 times respectively; p < 0.001), drinking time (1.50 times; p < 0.001), chews per minute, bolus and chews per bolus (1.12, 1.45 and 1.51 times; p < 0.001). From the 15th day before the diagnosis of SCK, rumination time in health cows was greater than that in SCK cows from −0.96% (−17 day) to 187.79% (0 days, < 0.001). We estimated the greater average value of drinking time in healthy cows compared with SCK cows from 34.22% on day −17 to −121.67% on day 0 (p < 0.001). Decrease in rumination time was associated with a significant increase in the probability of risk of SCK. Further studies are needed with a larger number of cows with SCK.

Effects of a Multicomponent Herbal Extract on the Course of Subclinical Ketosis in Dairy Cows - a Blinded Placebo-controlled Field-study.

A blinded placebo-controlled multi-center on-farm trial was conducted in dairy cows with subclinical ketosis to investigate effects of a multicomponent herbal extract. Blood ketone levels were measured weekly in early lactating cows from 16Swiss herds. Cows were subclassified based on their initial blood-β-hydroxybutyrate levels (≥ 1.0 [KET-low, 84 cows] and > 1.2 mmol/L [KET-high, 39 cows]) and randomly distributed to 3 groups treated orally with herbal extract containing Camellia sinensis, Cichcorium intybus, Gentiana lutea, Glycyrrhiza glabra, Taraxacum officinale, Trigonella foenum-graecum, and Zingiber officinale, sodium propionate, or placebo twice a day for 5 days. Milk yield, milk acetone, blood-β-hydroxybutyrate, glucose, nonesterified fatty acids, gamma-glutamyl transferase, and glutamate dehydrogenase were analyzed over 2 wk. Linear mixed effect models were used for data analysis. No effects were found for nonesterifed fatty acids, gamma-glutamyl transferase, and glucose. Significantly higher glutamate dehydrogenase (29.71 U/L) values were found in herbal extract-treated animals compared to sodium propionate on day 7 (22.33 U/L). By trend, higher blood-β-hydroxybutyrate levels (1.36 mmol/L) were found in the placebo group of KET-high-cows on day 14 compared to the sodium propionate group (0.91 mmol/L). Milk yields of all treatment groups increased. Milking time and treatment showed a significant interaction for milk acetone: sodium propionate led to an immediate decrease, whereas herbal extracts resulted in a milk acetone decrease from day 7 on, reaching significantly lower milk acetone on day 14 (3.17 mg/L) when compared to placebo (4.89 mg/L). In conclusion, herbal extracts and sodium propionate are both likely to improve subclinical ketosis in dairy cows, however, by different modes of action.

Estimating the combined costs of clinical and subclinical ketosis in dairy cows.

Clinical ketosis (CK) and subclinical ketosis (SCK) are associated with lower milk production, lower reproductive performance, an increased culling of cows and an increased probability of other disorders. Quantifying the costs related to ketosis will enable veterinarians and farmers to make more informed decisions regarding the prevention and treatment of the disease. The overall aim of this study was to estimate the combined costs of CK and SCK using assumptions and input variables from a typical Dutch context. A herd level dynamic stochastic simulation model was developed, simulating 385 herds with 130 cows each. In the default scenario there was a CK probability of almost 1% and a SCK probability of 11%. The herds under the no risk scenario had no CK and SCK, while the herds under the high-risk scenario had a doubled probability of CK and SCK compared to the default scenario. The results from the simulation model were used to estimate the annual cash flows of the herds, including the costs related to milk production losses, treatment, displaced abomasum, mastitis, calf management, culling and feed, as well as the returns from sales of milk and calves. The difference between the annual net cash flows of farms in the no risk scenario and the default scenario provides the estimate of the herd level costs of ketosis. Average herd level costs of ketosis (CK and SCK combined) were €3,613 per year for a default farm and €7,371 per year for a high-risk farm. The costs for a single CK case were on average €709 (with 5 and 95 percentiles of €64 and €1,196, respectively), while the costs for a single SCK case were on average €150 (with 5 and 95 percentiles of €18 and €422, respectively) for the default farms. The differences in costs between cases occurred due to differences between cases (e.g., cow culled vs cow not culled, getting another disease vs not getting another disease).

Combination of Sensor Data and Health Monitoring for Early Detection of Subclinical Ketosis in Dairy Cows.

Subclinical ketosis is a metabolic disease in early lactation. It contributes to economic losses because of reduced milk yield and may promote the development of secondary diseases. Thus, an early detection seems desirable as it enables the farmer to initiate countermeasures. To support early detection, we examine different types of data recordings and use them to build a flexible algorithm that predicts the occurence of subclinical ketosis. This approach shows promising results and can be seen as a step toward automatic health monitoring in farm animals.

Diagnosis of subclinical ketosis in dairy cows

Ketosis is a common disease in high producing dairy cows during the early lactation period. Subclinical ketosis (SCK) and periparturient diseases considerably account for economic and welfare losses in dairy cows. Subclinical ketosis poses an increased risk of production-related diseases such as clinical ketosis, displaced abomasum, retained placenta, lameness, mastitis and metritis. Production efficiency decreases (lower milk production, poor fertility, and increased culling rates), which results in economic losses. Increased concentrations of circulating ketone bodies, predominantly ?-hydroxybutyrate (BHB), without the presence of clinical signs of ketosis are considered as SCK. It is characterized by increased levels of ketone bodies in the blood, urine and milk. The gold standard test for ketosis is blood BHB. This ketone body is more stable in blood than acetone or acetoacetate. The most commonly used cut-points for subclinical ketosis are 1.2 mmol/L or 1.4 mmol/L for BHB in the blood. Clinical ketosis generally involves much higher levels of BHB, about 3.0 mmol/L or more. Usually, detection of SCK is carried out by testing ketone body concentrations in blood, urine and milk. A variety of laboratory and cowside tests are available for monitoring ketosis in dairy herds. But no cowside test has perfect sensitivity and specificity compared to blood BHB as the gold standard test. The aim of this review is to overview diagnostic tests for SCK in dairy cows, including laboratory and cowside tests.

Methylglyoxal: A newly detected and potentially harmful metabolite in the blood of ketotic dairy cows

Ketosis causes serious economic losses for the modern dairy industry because it is a highly prevalent metabolic disease among cows in high-producing herds during the transition period. Due to some striking similarities between diabetes in humans and ketosis in dairy cows, there is potential for the use of methylglyoxal (MGO)-commonly used in human diabetics-as a biomarker in dairy cattle. However, currently no data are available about the presence of MGO in the serum of dairy cattle or about the characteristics of its production or its potential contribution in the pathogenesis of ketosis. To determine the potential origin and pathway of formation of MGO, cows in different metabolic conditions [i.e., non-subclinically ketotic dairy cows in early lactation (n = 7), subclinically ketotic dairy cows in early lactation (n = 8), overconditioned dry cows (BCS >4.25, n = 6), and nonlactating heifers (n = 6)] were selected. Serum MGO concentrations were determined and correlated with indicators of the glucose and lipid metabolism and with haptoglobin (Hp) as an inflammatory marker. The serum MGO concentrations in subclinically ketotic cows (712.60 ± 278.77 nmol/L) were significantly greater than in nonlactating heifers (113.35 ± 38.90 nmol/L), overconditioned dry cows (259.71 ± 117.97 nmol/L), and non-subclinically ketotic cows (347.83 ± 63.56 nmol/L). In serum of lactating cows, concentrations of glucose and fructosamine were lower than in heifers and were negatively correlated with MGO concentrations. Even so, concentrations of metabolic and inflammatory markers such as dihydroxyacetone phosphate, nonesterified fatty acids, β-hydroxybutyrate, acetone, and Hp were remarkably higher in subclinically ketotic cows compared with nonlactating heifers; these metabolites were also positively correlated with MGO. In human diabetics elevated MGO concentrations are stated to originate from both hyperglycemia and the enhanced lipid metabolism, whereas higher MGO concentrations in subclinically ketotic cows were not associated with hyperglycemia. Therefore, our data suggest MGO in dairy cows to be a metabolite produced from the metabolization of acetone within the lipid metabolization pathway and from the metabolization of dihydroxyacetone phosphate. Furthermore, the highly positive correlation between MGO and Hp suggests that this reactive compound might be involved in the proinflammatory state of subclinical ketosis in dairy cows. However, more research is needed to determine the potential use of MGO as a biomarker for metabolic failure in dairy cows.

The impact of subclinical ketosis in dairy cows on greenhouse gas emissions of milk production

The dairy sector is an important contributor to greenhouse gas (GHG) emissions. Subclinical ketosis (SCK), a metabolic disorder in dairy cows, increases the risk of other diseases. SCK can increase GHG emissions per kg of milk produced by reducing production efficiency of dairy herds. With an expected increase in milk consumption, and potential new policies to reduce GHG emissions from agriculture, producing efficiently and reducing GHG emissions becomes increasingly important. The objective of this study was to estimate the impact of SCK and related diseases (i.e. mastitis, metritis, displaced abomasum, lameness, and clinical ketosis) on GHG emissions of milk production. To this end, a dynamic stochastic simulation model was developed and combined with life cycle assessment (LCA). This model simulates the dynamics of SCK and related diseases, and the associated production losses (reduced milk production, discarded milk, a prolonged calving interval, and removal (culling or dying on the farm) per cow during one lactation. Subsequently, an LCA was performed to quantify the impact of SCK and related diseases on GHG emissions per ton fat-and-protein-corrected milk (kg CO2equivalents/t FPCM) from cradle to farm gate. The emissions of GHGs increased on average by 20.9 kg CO2e/t FPCM per case of SCK, with a range between 6.8 and 48.0 (5–95 percentiles). This increase in emissions was caused by a prolonged calving interval (31%), discarded milk (30%), reduced milk production (19%), and removal of cows (20%). Most cows had SCK only (61%); for these cows emissions increased by 7.9 kg CO2e/t FPCM, whereas emissions of cows that died on farm increased by 463.0 kg CO2e/t FPCM. Sensitivity analysis showed that the disease incidence, removal risk, relations of SCK with other diseases, and emission factors related to feed production, enteric fermentation, and manure resulted in a high variation of GHG emissions. This study is the first study that estimated the impact of SCK on GHG emissions and showed the relation between cow health and GHG emissions of milk production.