METHOD AND DEVICE FOR MONITORING THE HEALTH STATUS OF DAIRY COWS

Description

[0001] The present invention relates to methods and devices for monitoring the health status of dairy cows, in particular entire dairy herds, according to claims 1 to 12. The method is based on the analysis of the biomarkers haptoglobin (HP) and a part of the polymeric immunoglobulin receptor (PIGR), the secretory component ( Secretory Component SC), in a milk sample. In particular, the claimed method and the device of the invention, based on the protein biomarkers HP and PIGR (SC) described herein, enable the diagnosis of systemic diseases that occur outside the udder. Thus, the invention makes it possible to regularly monitor the general health status of a dairy herd. The present invention relates to non-invasive diagnostic methods, as well as devices and diagnostic kits for carrying out these methods. DESCRIPTION

[0002] The latest technological methods enable increasingly large dairy herds to be managed cost-effectively. Furthermore, the need for personnel can be drastically reduced, for example, through the use of automatic milking systems. However, this can limit or even eliminate the daily health checks of the cows. One solution to this problem would be the automation of health monitoring. This can be achieved by detecting specific health markers in the milk. Acute-phase proteins such as HP are suitable as such markers, as their concentrations rise very rapidly in the early stages of an immune response. Currently, the measurement of HP in milk is not routinely used in either agriculture or veterinary laboratory diagnostics.

[0003] Post-calving health monitoring is carried out by the herd manager or trained farm staff through a clinical examination 7-10 days after calving and a puerperal check (between days 20 and 28 post-calving). General condition, body temperature, lochia, milk production, and, if necessary, milk or urine ketones are assessed. Udder health is checked daily in the milking parlor by the milking staff. During monthly milk recording, parameters such as somatic cell count, urea content, and milk fat and protein content are collected to assess udder health and the metabolic status of the individual animal and the herd. An increasing number of farms are able to record the somatic cell count at each milking using automatic milking systems. Various methods exist for the routine recording of health parameters.However, these can only provide information about the metabolic state, a specific disease, or the udder health of the animal.

[0004] Monitoring the health of animals on farms is therefore highly subjective and time-consuming. Current solutions involve recording and analyzing combinations of parameters (milk conductivity, milk yield, movement patterns, lying times, progesterone concentration, ketone bodies, lactate dehydrogenase, milk fat, protein, lactose, and urea) using appropriate measuring technology and multifunctional herd management programs, such as FullExpert (Lemmer-Fullwood). This allows for the detection of animals with abnormalities (heat, lameness, miscarriage, displaced abomasum, ketosis, mastitis). However, these systems are complex and expensive to purchase. In critical cases, a veterinarian is consulted.Measuring clinical-chemical, metabolic, and endocrinological parameters in the blood of animals is a routine examination in veterinary laboratory diagnostics, but can only be performed by a veterinarian on selected, abnormal, or already diseased animals. Routinely used parameters in clinical chemistry can only provide a comprehensive picture of the cattle's condition when considered in combination.

[0005] Approaches already exist to assess the health of dairy cows more easily, quickly, and objectively. This is done by measuring acute-phase proteins in the blood or milk. Hepatitis (HP) is the most frequently studied acute-phase protein in cattle. During mastitis, the HP concentration in milk is also significantly elevated. However, HP in milk has so far only been discussed as a potential indicator of mastitis.

[0006] SC is not an acute-phase protein, but rather a component of a transmembrane receptor for polymeric immunoglobulins, the PIGR, on secretory epithelial cells of the mucous membranes and also in the udder. Upon binding of polymeric immunoglobulin (Ig)A or IgM, the antibody-receptor complex is transported via transcytosis from the lateral to the apical side of the epithelium. There, the receptor is enzymatically cleaved, releasing SC and IgA or IgM. In this way, IgA is transported into the milk. During periparturient immunosuppression and in early lactation, dairy cows are also particularly susceptible to infectious diseases that do not affect the udder (systemic diseases) such as uterine, hoof, or respiratory infections. Milk mite infestation (LMV) is also not uncommon. Diagnostic tests for these diseases are currently routinely performed on the animals' blood. This requires a veterinary examination and treatment of the animal.Since a milk sample can be taken with significantly less effort, it is in the economic interest of the farmer to be able to detect systemic diseases in this sample medium.

[0007] Grönlund et al. (2005) revealed the potential of haptoglobin and serum amyloid A (SAA) in milk as indicators of chronic subclinical mastitis and observed significant variation in haptoglobin and SAA concentrations in milk from udder quarters with chronic subclinical mastitis.

[0008] Kalmus et al. (2013) demonstrates the relationship between quantitative bacteriological results from a real-time PCR test and the concentrations of acute-phase proteins (APP) and N-acetyl-β-d-glucosaminidase (NAGase) activity in milk in naturally occurring clinical mastitis.

[0009] Boehmer et al. (2010) reveals proteomic approaches to conduct comparative analyses of milk from healthy cows and cows with clinical mastitis, as well as of proteins identified in mastitic milk.

[0010] WO 01 / 27631 A1 concerns an assay for the detection of haptoglobin (Hp) in milk as a means of predicting or detecting mastitis or subclinical mastitis, e.g., in dairy cattle.

[0011] Mansor (2012) shows changes in milk proteomes, peptidomes and metabolomes during the course of bovine mastitis compared to normal milk samples and investigated new biomarkers for bovine mastitis.

[0012] Makimura et al. (1982) discloses the measurement of haptoglobin in serum for the detection of severe inflammatory diseases such as mastitis, polymetra, or traumatic reticulitis, for which a modified method by Tarukoski was used.

[0013] Ashour et al. (1987) discloses a method for the routine determination of the rate of colorimetric enzyme reactions using a 96-well microtiter plate reader.

[0014] The health management departments of growing dairy farms are constantly seeking alternative solutions for herd monitoring. In practice, several parameters exist that, when measured automatically on the farm daily, allow for the detection of cows with abnormalities (heat, lameness, miscarriage, milk fever, ketosis, mastitis). However, no validated biomarker currently exists for analyzing milk to assess overall health. Analyzing milk significantly simplifies sampling, thus enabling on-farm measurement. A key advantage is that, unlike blood sampling, this method does not require a veterinarian, which reduces costs and minimizes disruption to the overall agricultural production process.Accordingly, the object of the present invention is to provide new approaches for health monitoring of dairy cow herds, which enables simplified and routine control of large dairy cattle herds without the involvement of a veterinarian.

[0015] In a first aspect, the task is solved by a non-invasive procedure for monitoring the health status of a dairy cow, whereby the procedure is not used for diagnosing mastitis, comprising the following steps: (a) Providing a milk sample from the dairy cow, (b) Measuring the concentration of the biomarker haptoglobin (HP) in the milk sample, (c) Comparing the measured concentration from (b) with a reference value of the measured biomarker, wherein a deviation of the measured concentration from the reference value indicates an unhealthy condition of the dairy cow, and wherein the health condition includes mild systemic disease, mild systemic disease with displaced abomasum (LMV), severe systemic disease, or combinations of these conditions.

[0016] In a second aspect, the task is solved by a non-invasive procedure for monitoring the health status of a dairy cow, comprising the following steps: (a) Providing a milk sample from the dairy cow, (b) Measuring the concentration of the biomarkers haptoglobin (HP) and secretory component of the polymeric immunoglobulin receptor (PIGR (SC)) in the milk sample, (c) Comparing the measured concentration from (b) with a reference value of the at least one measured biomarker, wherein a deviation of the measured concentration from the reference value indicates a non-healthy condition of the dairy cow.

[0017] Preferably, the non-invasive procedure is used completely ex vivo or in vitroThe procedure was carried out. It is particularly important to emphasize that the biomarkers of the present invention are examined in a milk sample, so that the procedure is independent of invasive sampling and thus independent of a veterinarian. This allows the present procedure to be extended to large dairy herds and to regular (monthly) testing, which is not economically feasible, for example, by checking biomarkers in a blood sample.

[0018] The terms "protein biomarker," "biomarker," and "marker" are used synonymously in this description. These terms preferably refer to the concentration of individual or combinations of biological molecules, such as proteins, nucleic acids, carbohydrates, etc. In particular, this disclosure deals with proteins as biomarkers. Insofar as the disclosure relates to a measurement of the biomarker concentration, this includes both direct measurement of the concentration (number of protein molecules per volume or weight) and indirect measurement. Thus, degradation products of the protein markers of the invention can also be measured, or alternatively, their concentration can be deduced based on their biochemical characteristics. Enzymes, for example, can be determined by detecting their enzymatic activity.

[0019] If necessary, the method of the present invention may optionally include in step (b) the measurement of at least one further biomarker. The at least one further biomarker is preferably selected from the group consisting of S100 calcium-binding protein A9 (S 100A9), interleukin (IL-) 18, tumor necrosis factor (TNF)-alpha, lactoferrin (LTF), and Vascular endothelial growth factor (VEGF) selected.

[0020] A further optional and preferred embodiment of the present invention is a method wherein step (b) comprises measuring a combination of at least three biomarkers, namely HP, PIGR (SC) and a third biomarker selected from the group consisting of S100A9, IL-18, TNF-alpha, LTF, and VEGF. The specificity and sensitivity of the method can be further improved by measuring additional biomarkers.

[0021] A preferred method is described herein, wherein a deviation of the measured concentration of the biomarkers HP and PIGR (SC) from the reference value indicates a systemic disease in the dairy cow, preferably a systemic disease that does not occur, or does not occur exclusively, in the udder, for example, mild systemic disease, mild systemic disease with LMV, severe systemic disease, or combinations of these diseases. Preferably, the measured deviation is an increase in the concentration of the biomarkers in the sample from a diseased cow compared to a healthy cow.

[0022] The present invention can be used to diagnose a systemic disease, preferably outside the udder, based on the disclosed biomarkers. Alternatively, the invention also relates to the diagnosis of mastitis based on the disclosed biomarkers. For this aspect, in one embodiment of the invention, it is preferred that the diagnosis of mastitis in a dairy cow is carried out by determining a combination of the biomarkers HP and PIGR (SC).

[0023] The term "reference value" is to be interpreted broadly here and encompass a variety of possible comparative values. An appropriate reference value is selected depending on the diagnostic objective. For the detection of diseased animals, the reference value could, for example, be a value of the biomarker in a healthy cow. However, if the procedure is used to monitor the course of a disease or to monitor the treatment of a diseased cow, the reference value could also represent a concentration of the biomarker in the milk of the monitored cow at an earlier point in time—for example, before the start of treatment. In particular, however, it is preferred that the reference value be a cut-off value, whereby if the measured concentration of the biomarker exceeds the cut-off value, an unhealthy condition of the dairy cow is indicated.Depending on the chosen cut-off value, an unhealthy condition can be detected if the test result is greater than or equal to the cut-off. A threshold value can also be determined for a specific herd, whereby a healthy dairy herd under monitoring is regularly tested for the biomarker, and a "healthy" reference value specific to the herd is established based on these results. If, during regular herd monitoring, a cow shows an elevated concentration of the biomarker, an unhealthy condition of that cow can be assumed.

[0024] In preferred embodiments of the present invention, the cut-off values ​​for distinguishing healthy from sick animals can be selected such that the corresponding biomarker has a specificity of at least 90% or higher, preferably 92%, and more preferably 94% or higher, with a sensitivity of at least 50%, preferably at least 60%, 70%, or at least 80%. For example, the cut-off value for the HP marker can be approximately 0.4 µg / ml, preferably approximately 0.5 µg / ml, and most preferably approximately 0.58 µg / ml. The cut-off value for the PIGR (SC) marker can be approximately 5 µg / ml, preferably approximately 8 µg / ml, and most preferably approximately 8.2 µg / ml. The cut-off value for the LTF marker can be approximately 80 µg / ml, preferably approximately 100 µg / ml, and most preferably approximately 120 µg / ml.The cutoff value for the marker VEGF can, for example, be approximately 7 µg / ml, preferably approximately 9 µg / ml, and most preferably approximately 9.5 µg / ml. The term "approximately" in connection with numerical values ​​preferably allows a deviation of + / - 20% of the stated value, more preferably a deviation of + / - 15%, + / - 10%, and most preferably + / - 5%.

[0025] As mentioned above, the inventive non-invasive method is suitable for monitoring the treatment of a dairy cow, with a decrease in the biomarker concentration during or after treatment indicating therapeutic success. Since an increase in the biomarker(s) described here in the milk correlates with a deterioration in the health of a dairy cow, the method can also be used to monitor the treatment success of individual sick animals. The reference value against which a measured concentration of the biomarker(s) of the invention in the milk sample is compared is, for example, a concentration of the biomarker(s) in the milk of the same cow at an earlier time, such as at the beginning of the treatment.

[0026] In some embodiments, it is preferred that the concentration of the biomarker HP is determined in an undiluted milk sample. In other embodiments, the milk sample is a milk sample containing preservatives (e.g., in milk performance testing).

[0027] The method of the present invention can be used, in particular, for routine monitoring of a dairy cow or dairy herd. Therefore, it is preferred that the non-invasive method be carried out regularly, preferably monthly, more preferably weekly, and even more preferably several times a week, up to daily.

[0028] In the inventive method, the milk sample to be examined is preferably taken non-invasively from the milk during milking. With fully automated milking systems, the milk sample can be taken directly from the milk (automatically). The milk sample thus obtained is then used for the inventive method described herein.

[0029] To measure the concentration of the biomarkers of the present invention, various technical methods known to those skilled in the art can be used. In particular, the present disclosure should not be considered limited to individual analytical methods. The determination of the concentration of biomarkers in a milk sample can be carried out biochemically by measuring the concentration using a method selected from sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS-PAGE), Fast Protein Liquid Chromatography (FPLC), and High Performance Liquid Chromatography (HPLC) or measuring the concentration immunologically using a method selected from Enzyme-linked immunosorbent assay (ELISA), enzyme immunoassay (EIA), fluorescence immunoassay (FIA), chemiluminescence immunoassay (CIA), radioimmunoassay (RIA), Western blot, and peptide arrays, or measuring the concentration spectrometrically using a method selected from Surface -Plasmon resonance (SPR), Matrix-assisted laser desorption / Ionization (MALDI) or Electrospray Ionization (ESI). Immunological methods are particularly preferred, for example using monoclonal (preferred) or polyclonal antibodies, for example in an ELISA.

[0030] The described method is intended, in particular, for monitoring the health of one or more dairy cows. Therefore, in some embodiments, the method is intended to be used to detect an unhealthy condition in a dairy cow. A specific diagnosis of the disease present in the dairy cow classified as unhealthy is not strictly necessary in this embodiment. Rather, the aim is to determine, by carrying out further subsequent diagnostic procedures, which disease the identified unhealthy dairy cow is suffering from. In these embodiments, the method of the present disclosure is aimed at the earliest possible detection of conspicuous animals through regular examinations according to the present invention. In the event of an unhealthy condition being detected, a veterinarian can subsequently be consulted to establish a specific diagnosis.

[0031] In some embodiments, the present disclosure relates to a non-invasive method for monitoring the health status of a dairy herd on a dairy farm, comprising regularly performing a method for monitoring the health status of at least one, preferably each, dairy cow of the dairy herd according to the non-invasive method for monitoring the health status of a dairy cow described herein.

[0032] A dairy herd is defined as a dairy herd within a dairy farm, preferably consisting of at least two animals, and more preferably of at least five, ten, fifteen, twenty, fifty, or more. The present invention is particularly useful for monitoring dairy herds with more than ten animals.

[0033] The aforementioned task is further enhanced in another aspect by a non-invasive system and / or device for monitoring the health status of a dairy cow, comprising: (a) Means for taking a milk sample from the dairy cow, (b) Antibodies for immunological measurement of the concentration of the biomarkers HP and PIGR (SC) in the milk sample, (c) Means for comparing the measured concentration from (b) with a reference value of the measured biomarker, wherein a deviation of the measured concentration from the reference value indicates an unhealthy condition of the dairy cow, solved.

[0034] In some embodiments, the non-invasive system and / or device may further include means for storing data and / or means for optically displaying data, such as a screen.

[0035] In some embodiments, the non-invasive system and / or device includes means for outputting information. These means are suitable for outputting information when a dairy cow is detected as unhealthy. For example, an optical or acoustic alarm can be triggered when a dairy cow is detected as unhealthy. Preferably, at least one piece of information regarding the identity of the identified unhealthy dairy cow is provided.

[0036] Preferably, the system and / or device described here is connected to a milking system and comprises means that, during a milking process, transfer a milk sample from the dairy cow from the milking system to the means for receiving a milk sample. Automated milking systems that enable milk sampling are known to those skilled in the art.

[0037] The non-invasive system and / or device according to the present invention preferably further comprises antibodies for measuring the concentration of at least one further biomarker selected from the group consisting of S100A9, IL-18, TNF-alpha, LTF, and VEGF.

[0038] The non-invasive system and / or device according to the present invention is therefore suitable for carrying out a method described herein.

[0039] The object of the invention is further solved by a diagnostic kit for monitoring the health status of a dairy cow, comprising antibodies for immunological determination or measurement of the concentration of the biomarkers HP and PIGR (SC) in a milk sample.

[0040] The diagnostic kit according to the invention comprises, in some embodiments, means for carrying out a procedure selected from EIA, FIA, CIA, RIA, Western blot, and peptide arrays. In particular, the diagnostic kit is suitable for carrying out a procedure described herein for monitoring the health status of dairy cows.

[0041] The present invention will now be further described by means of non-limiting examples.

[0042] The illustrations show: Figure 1: mRNA expression of selected markers in milk cells (MC) and blood leukocytes (BL) from cows in different disease states. The marker concentrations were determined by qPCR and are given as the percentage of expression of the reference genes cyclophilin B (PPIB) and ubiquitously expressed transcript (UXT). system.: systemic; disease.: disease; MC: milk cells; BL: blood leukocytes; * 0.05 > p > 0.01, and ** p ≤ 0.01. Figure 2:Concentrations of potential protein biomarkers in milk. Concentrations were determined using commercially available ELISA kits. nd = not detectable; system.: systemic; Erkrank.: disease; * 0.05 > p > 0.01, and ** p ≤ 0.01. Figure 3: Correlations of the concentrations of the biomarkers HP and LTF in milk and plasma. Concentrations were determined using commercially available ELISA kits. Positive correlations are indicated by the regression lines. Figure 4: ROC curves of selected milk biomarkers. A: ROC analysis of individual markers in various disease states. B: Combined ROC analysis of all diseased animals. systemic: systemic; disease: disease EXAMPLES Materials and methods: Quantification of protein biomarkers in milk and plasma

[0043] Selected proteins in milk and plasma were quantified using commercially available ELISA kits. All HP measurements were performed on undiluted samples, as this is sufficient to detect fluctuations in the HP marker at different stages of the disease. Pre-coated plates were incubated with 100 µl of sample (30 min, room temperature (RT)). Purified HP (LeeBioSolutions, St. Louis, Missouri, USA) was used as a standard in the range of 0.125 µg / ml. The plate was washed three times in assay wash buffer and then incubated with 100 µl of peroxidase-conjugated anti-HP antibody diluted 1:40 (30 min, RT). After three washing steps, 100 µL of ready-to-use tetramethylbenzidine substrate solution (Moss Inc., Pasadena, Maryland, USA) was added, and the mixture was incubated at room temperature for 10 to 30 minutes. The reaction was stopped with 50 µL of 9.9% H₃PO₄.

[0044] PIGR (SC) was quantified using an ELISA kit for the detection of bovine PIGR (Life Science USCN Inc.) according to the manufacturer's instructions. Milk was diluted 1:300 to 1:1,000 for control samples and 1:5,000 to 1:10,000 for samples from diseased cows. Plasma samples were diluted 1:100,000. Statistical analysis

[0045] The analysis of the differences between the groups, Spearman rank correlations, Receiver operating characteristic ROC analysis and visualization of the results were performed using SigmaPlot11 software (Systat Software, Erkrath, Germany). To avoid unwanted statistical biases, animal samples were randomly selected for quantitative analysis. real-timeRT-PCR (qPCR) or ELISA was selected. Data sets were analyzed for normal distribution. If the Shapiro-Wilk test was positive, a t-test was performed. If data were not normally distributed, the Mann-Whitney U rank-sum test was used. All affected groups were compared to the control group. Data from different non-udder disorders were combined when a small number of samples were tested. P-values ​​are defined as follows: * 0.05 > p > 0.01, and ** p ≤ 0.01. Selection and evaluation of potential biomarkers

[0046] ROC analysis was used to evaluate the discriminatory power of the biomarkers. An area under the curve ( Area Under the Curve,AUC values ​​> 0.9 were considered highly discriminatory and AUC values ​​< 0.6 were considered non-discriminatory. Biomarkers were selected based on their ability to best differentiate between mild systemic disease and the control group. Statistical analysis of biomarkers and marker combinations was performed using the Open source data mining software TANAGRA was performed. To prevent possible Overfitting To prevent cross-validation, (Cross Validation, CV testing was performed (10 times, 1 repetition). Sensitivity, specificity, and substitution error rate were obtained from the CV tests. Different diseases were grouped together. Biomarkers or combinations thereof were evaluated based on their ability to differentiate diseased cows. Example 1: Differential gene expression of biomarkers in milk

[0047] The mRNA expression of individual biomarkers in mammary cells was analyzed using qPCR. To confirm the systemic significance of potential biomarkers from the local mammary gland environment, the expression pattern of these biomarkers in peripheral blood leukocytes was also examined. Data from mild and severe systemic disease groups were combined and tested in cases of small sample sizes within a single systemic disease group. Figure 1 shows the results of the most relevant biomarkers. Example 2: Quantification and selection of biomarkers

[0048] Based on the results of previous experiments (microarray, qPCR, and others), potential biomarkers were selected and quantified at the protein level using commercial ELISA kits. Elevated concentrations of IL-18, LTF, PIGR (SC), TNF-alpha, and VEGF were found in milk in cases of LMV, severe systemic disease, mastitis, and combinations of these conditions. However, HP and S100A9 showed elevated levels in cases of mild systemic disease ( Figure 2 Expression patterns of HP, IL-18, and LTF were also determined in plasma to assess the significance of these markers for systemic diseases. The correlation between milk and plasma HP and LTF concentrations is described in Figure 3The positive Spearman correlation coefficients (Spearman ρ) demonstrate the relationship between milk and plasma protein concentrations. Additionally, the correlation of the strongest biomarkers in milk was investigated. All proteins showed a positive correlation between their concentration in milk and the presence of disease (Table 1). The strongest markers were further evaluated statistically. Table 1: Correlations of protein biomarkers in milk and plasma Correlation of Spearman correlation coefficient p n Correlation in milk Milk HP and Milk PIGR (SC) 0,67 0,001 71 Milk LTF and Milk PIGR (SC) 0,61 0,001 79 Milk HP and Milk LTF 0,59 0,001 142 Milk HP and milk VEGF 0,58 0,001 120 Milk LTF and milk VEGF 0,54 0,001 132 Milk VEGF and Milk PIGR (SC) 0,41 0,001 79 Correlation in milk and plasma Milk HP and Plasma HP 0,78 0,001 121 Milk IL-18 and plasma IL-18 0,38 0,088 21 Milk LTF and Plasma LTF 0,33 0,005 69 Correlation in plasma Plasma HP and Plasma LTF 0,59 0,001 63 Example 3: Statistical evaluation of biomarkers

[0049] For statistical analysis, the highly regulated and highly concentrated milk biomarkers HP, PIGR (SC), LTF, and VEGF were selected. A subset of samples in which all four markers were determined was used to allow for a direct comparison of the results. The biomarkers alone and combinations of two biomarkers were evaluated. Seventeen control samples and 49 samples from diseased cows were used. The ability to differentiate between disease groups was assessed using ROC analysis ( Figure 4A , Table 2). HP and PIGR (SC) showed the best differentiation of mild systemic disease with an AUC of 0.69 and 0.68, respectively. All proteins were highly discriminatory for severe systemic disease and mastitis (AUC > 0.9). Table 2: Differentiation capability of milk biomarkers for various diseases. Data were generated using ROC analysis. (Control: n = 17, Mild systemic disease: n = 17, Milk volume (+ metabolic disease): n = 8, Severe systemic disease: n = 5, Severe systemic disease + Milk volume: n = 8, Mastitis: n = 11) Control group vs. disease group AUC 95% confidence interval p HP Mild systemic disease. 0,69 0,48 - 0,89 0,065 LMV (+ metabolic disease) 0,96 0,89 - 1,03 < 0,001 Severe systemic disease. 0,99 0,95 - 1,03 0,001 Severe systemic illness + LMV 0,99 0,95 - 1,02 < 0,001 mastitis 1,00 1,00 - 1,00 < 0,001 PIGR (SC) Mild systemic disease. 0,68 0,49 - 0,87 0,071 LMV (+metabolic disease) 0,84 0,64 - 1,04 < 0,05 Severe systemic disease. 0,95 0,87 - 1,04 < 0,05 Severe systemic illness + LMV 0,80 0,61 - 0,99 < 0,05 mastitis 0,99 0,98 - 1,01 < 0,001 LTF Mild systemic disease. 0,67 0,48 - 0,86 0,088 LMV (+ metabolic disease) 0,82 0,62 - 1,03 < 0,05 Severe systemic disease. 0,95 0,86 - 1,05 < 0,05 Severe systemic illness + LMV 0,93 0,84 - 1,03 < 0,001 mastitis 0,98 0,95 - 1,02 < 0,001 VEGF Mild systemic disease. 0,57 0,38 - 0,77 0,459 LMV (+metabolic disease) 0,99 0,96 - 1,02 < 0,001 Severe systemic disease. 0,84 0,58 - 1,08 < 0,05 Severe systemic illness + LMV 0,96 0,90 - 1,03 < 0,001 mastitis 0,97 0,91 - 1,03 < 0,001

[0050] Marker combinations were evaluated for discrimination between sick and control animals using two statistical classification methods: multinomial logistic regression (MLR) and k-nearest neighbor (K-NN) classification (Table 4). A second statistical model was applied to avoid potential biases in the results. HP is the best choice for use as a single biomarker. In combination with PIGR (SC) or LTF, a slight increase in sensitivity or specificity can be achieved. These combinations showed the best results for the detection of sick animals.

[0051] Practical application of biomarkers requires high test specificity to avoid overestimating disease incidence in large dairy farms. Therefore, a combined ROC analysis was performed for all disease groups versus a control to evaluate the sensitivity (true positive), specificity (true negative), 1-sensitivity (false negative), and 1-specificity (false positive) of biomarker determination in milk at different cut-off concentrations. Table 3 shows the values ​​for possible cut-off concentrations at a high specificity of 94%. Corresponding ROC curves are shown in [reference missing]. Figure 4B As demonstrated, with a specificity of 94%, 6% of the actually healthy cows would be incorrectly identified as sick. 18%, 41%, 45%, and 33% of the sick animals would be correctly diagnosed as healthy when HP, PIGR (SC), LTF, and VEGF levels were measured, respectively.

[0052] Based on this analysis, it was therefore demonstrated that the determination of HP is suitable for detecting diseases in dairy cows. A combined measurement with PIGR (SC) or LTF is also possible to increase sensitivity or specificity. Table 3: Differentiation capability of milk biomarkers for diseased animals. The data were generated using ROC analysis. (Control: n = 17, diseased: n = 49) AUC 95% confidence interval p Cut-off at 94% specificity Sensitivity at 94% specificity / % HP 0,88 0,80 - 0,96 < 0,001 0.58 µg / ml 82 PIGR (SC) 0,82 0,72 - 0,93 < 0,001 8.20 µg / ml 59 LTF 0,84 0,74 - 0,94 < 0,001 120.7 µg / ml 55 VEGF 0,82 0,72 - 0,92 < 0,001 9.50 ng / ml 67 Table 4: Evaluation of milk biomarkers and their combinations. Classification was performed using MLR and K-NN: control (n = 17) vs. diseased (n = 49). Sensitivity, specificity, and substitution error rates were extracted from CV (10-fold, 1 replicate). Multinomial logistic regression k-nearest neighbors classification (Cross-validation) / % (Cross-validation) / % Marker (combination) Sensitivity Specificity Error rate Sensitivity Specificity Error rate Single marker HP 86 88 13 91 69 15 LTF 84 44 27 82 63 23 VEGF 84 38 28 73 31 38 PIGR (SC) 86 25 30 77 19 38 Marker combinations HP & VEGF 86 88 13 80 94 17 HP & PIGR (SC) 89 81 13 84 75 18 HP & LTF 89 69 17 86 81 15 VEGF & PIGR (SC) 86 63 20 82 56 25 LTF & PIGR (SC) 84 56 23 86 31 28 LTF & VEGF 82 56 25 84 44 27 List of abbreviations

[0053] AUC Area Under the Curve, Area under the curve (ABC) BLB Blood leukocytes CIA Chemiluminescence immunoassay CV Cross Validation, Cross-validation EIA Enzyme Immunoassay ELISA Enzyme-linked immunosorbent assay Disease. Disease ESI Electrospray Ionization FIAfluorescence immunoassay FPLC Fast Protein Liquid Chromatography HPHaptoglobin HPLC High Performance Liquid Chromatography Ig Immunoglobulin IL Interleukin K-NNk-nearest-neighbors classification LMV Abomasal displacement LTF Lactoferrin MALDI Matrix-assisted laser desorption / Ionization MLR multinomial logistic regression mRNA Messenger Ribonucleic acid MZ milk cells PIGR polymer immunoglobulin receptor PPIBCyclophilin B (reference gene) RIA radioimmunoassay ROC Receiver Operating Characteristics S100A9S100 Calcium-binding protein A9 SC Secretory Component secretory component of the PIGR SDS-PAGE Sodium Dodecyl sulfate polyacrylamide gel electrophoresis SPR Surface -Plasmon resonance system.systemic TNF-alpha tumor necrosis factor-alpha UXT Ubiquitously-Expressed Transcript, Ubiquitously expressed transcript (reference gene) VEGF Vascular endothelial growth factor

Claims

1. A non-invasive method for monitoring the state of health of a dairy cow, the method not being used to diagnose mastitis, comprising the steps of: (a) providing a milk sample from the dairy cow, (b) measuring the concentration of the biomarker haptoglobin (HP) in the milk sample, (c) comparing the measured concentration from (b) with a reference value for the measured biomarker, wherein a deviation of the measured concentration from the reference value indicates an unhealthy condition in the dairy cow, and wherein the state of health includes minor systemic disease, minor systemic disease with abomasal displacement (LMV), serious systemic disease, or combinations of these diseases.

2. The non-invasive method for monitoring the state of health of a dairy cow, comprising the steps of: (a) providing a milk sample from the dairy cow, (b) measuring the concentration of the biomarkers haptoglobin (HP) and the secretory component of the polymeric immunoglobulin receptor (PIGR (SC)) in the milk sample, (c) comparing the measured concentration from (b) with a reference value for the at least one measured biomarker, wherein a deviation of the measured concentration from the reference value indicates an unhealthy condition in the dairy cow.

3. The non-invasive method according to claim 1 or 2, wherein step (b) comprises the measurement of at least one further biomarker selected from the group consisting of LTF, S100A9, IL18, TNF-alpha, and VEGF.

4. The non-invasive method according to any one of claims 1 to 3, wherein a deviation of the measured concentration from the reference value indicates a systemic disease in the dairy cow.

5. The non-invasive method according to any one of claims 1 to 4, wherein the reference value is a threshold value (cut-off), and wherein if the measured concentration of the biomarker exceeds the threshold value, an unhealthy condition is determined to exist in the dairy cow.

6. The non-invasive method according to any one of claims 1 to 5, wherein the method is performed regularly, preferably monthly.

7. The non-invasive method according to any one of claims 1 to 6, wherein in step (b), the concentration is measured biochemically using a method selected from SDS-PAGE, FPLC and HPLC, measured immunologically using a method selected from ELISA, EIA, FIA, CIA, RIA, western blot and peptide array, or measured spectrometrically using a method selected SPR, MALDI or ESI.

8. The non-invasive method for monitoring the state of health of a dairy herd in a dairy farm, comprising regularly performing a method for monitoring the state of health of at least one, preferably each, dairy cow in the dairy herd according to any one of claims 1 to 7.

9. The non-invasive system and / or apparatus for monitoring the state of health of a dairy cow, comprising: (a) means for collecting a milk sample from the dairy cow, (b) antibodies for immunological measurement of the concentration of the biomarkers HP and PIGR (SC) in the milk sample, (c) means for comparing the measured concentration from (b) with a reference value for the measured biomarker, wherein a deviation of the measured concentration from the reference value indicates an unhealthy condition in the dairy cow.

10. The non-invasive system and / or apparatus according to claim 9, further comprising means for storing data and / or means for visually displaying data, such as a screen.

11. The non-invasive system and / or apparatus according to claim 9 or 10, wherein the system and / or apparatus is connected to an automated or semi-automated milking plant (milking system) and comprises means that transfer a milk sample from the dairy cow during the milking process to the means for collecting a milk sample.

12. A diagnostic kit for monitoring the state of health of a dairy cow, comprising antibodies for immunologically determining the concentration of the biomarkers HP and PIGR (SC) in a milk sample.