Classifications

• • C—CHEMISTRY; METALLURGY
  • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
  • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
  • C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
  • C12Q1/26—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving oxidoreductase
  • C12Q1/32—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving oxidoreductase involving dehydrogenase
• • C—CHEMISTRY; METALLURGY
  • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
  • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
  • C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
  • C12Q1/48—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving transferase
• • C—CHEMISTRY; METALLURGY
  • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
  • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
  • C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
  • C12Q1/533—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving isomerase
• • C—CHEMISTRY; METALLURGY
  • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
  • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
  • C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
  • C12Q1/54—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving glucose or galactose
• • G—PHYSICS
  • G01—MEASURING; TESTING
  • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
  • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
  • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
  • G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
  • G01N33/68—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving proteins, peptides or amino acids
  • G01N33/6803—General methods of protein analysis not limited to specific proteins or families of proteins
  • G01N33/6827—Total protein determination, e.g. albumin in urine
• • G—PHYSICS
  • G01—MEASURING; TESTING
  • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
  • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
  • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
  • G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
  • G01N33/92—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving lipids, e.g. cholesterol, lipoproteins, or their receptors
• • G—PHYSICS
  • G01—MEASURING; TESTING
  • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
  • G01N2800/00—Detection or diagnosis of diseases
  • G01N2800/50—Determining the risk of developing a disease

Definitions

• the present invention relates to methods and systems for predicting the risk of physiological imbalance in an animal.
• the animal may be a cow.
• the methods and systems of the invention rely on a sample of a body fluid of the animal, such as urine, blood or milk.
• Prior art has focused on systems and methods for observing and predicting a physiological state of an animal.
• the present invention is focusing on the situation of marked deviations in physiological parameters may lead to a general increased risk of disease and reduced performance or reproduction and that prevention of these deviations will prevent the risk of disease and reduced performance and/or reproduction for that animal.
• the inventors of the present invention have realized that early detection for physiological imbalance and subsequent prevention of this physiological imbalance can help prevent an animal from entering into a physiological state that relates to sub-clinical or clinical diseases or suboptimal performance and/or reproduction.
• an object of the present invention relates to a method for predicting the risk of physiological imbalance in an animal.
• one aspect of the invention relates to a method for predicting the risk of physiological imbalance in an animal, said method comprises the steps of:
• the first risk indicator may be selected from the group consisting of free glucose, glucose-6-phosphate, glucose-l-phosphate, free galactose, UDP-glucose, UDP-galactose, glycerol 3-phosphate, beta- hydroxybutyric acid (BHBA), isocitrate, citrate, malate, , 2-oxo-glutarate, isocitrate dehydrogenase, malate dehydrogenase, pyruvate carboxylase, glycogen phosphorylase, glutamate dehydrogenase, glyceraldehyde 3- phosphate dehydrogenase, aspartate aminotransferase, aldehyde dehydrogenase, phosphoglucomutase, triosephosphate isomerase, lactate, cholesterol, non-esterified fatty acids (NEFA), milk protein, milk fat, and milk lactose;
• NEFA non-esterified fatty acids
• Another aspect of the present invention relates to a system for predicting the risk of physiological imbalance in an animal, the system comprising :
• a computer comprising a processor and being operatively connected to a
• At least one sample providing device for repetitively providing at least one sample of a body fluid of the animal
• the first risk indicator may be selected from the group consisting of free glucose, glucose-6- phosphate, glucose-l-phosphate, free galactose, UDP-glucose, UDP- galactose, glycerol 3-phosphate, beta-hydroxybutyric acid (BHBA), isocitrate, citrate, malate, 2-oxo-glutarate, isocitrate
• BHBA beta-hydroxybutyric acid
• dehydrogenase malate dehydrogenase, pyruvate carboxylase, glycogen phosphorylase, glutamate dehydrogenase, glyceraldehyde 3-phosphate dehydrogenase, aspartate aminotransferase, aldehyde dehydrogenase, phosphoglucomutase, triosephosphate isomerase, lactate, cholesterol, non-esterified fatty acids (NEFA), milk protein, milk fat, and milk lactose;
• NEFA non-esterified fatty acids
• (A2) optionally, determining the concentration or amount of at least one second risk indicator present in the sample, the at least one second risk indicator, is different from the first risk indicator and may be selected from the group consisting of free glucose, glucose- 6-phosphate, glucose-l-phosphate, free galactose, UDP-glucose, UDP-galactose, glycerol 3-phosphate, beta-hydroxybutyric acid (BHBA), isocitrate, citrate, malate, 2-oxo-glutarate, isocitrate dehydrogenase, malate dehydrogenase, pyruvate carboxylase, glycogen phosphorylase, glutamate dehydrogenase, glyceraldehyde 3-phosphate dehydrogenase, aspartate aminotransferase, aldehyde dehydrogenase, phosphoglucomutase, triosephosphate isomerase, lactate, cholesterol, non-esterified fatty acids (NEFA), milk
• (PI) determine the rate of change of the first indicator and, optionally, the at least one second indicator
• (P2) determine the level change of the first indicator and, optionally, the at least one second indicator and,
• (P3) compare said rate change and/or level change with one or more reference patterns of said first risk indicator and, optionally, the at least one second risk indicator, in order to predict the risk of physiological imbalance in the animal.
• Figure 1 shows descriptive statistics for milking data and variables measured in milk
• Figure 2 shows data for glucose 6-phosphate and free glucose measured in 3232 milk samples from Danish Friesian and Jersey cows. Parity x race significantly affected the glucose 6-phosphate as well as the free glucose content
• Figure 3 shows the comparisons between a colorimetric and a fluorometric analytical method.
• Mean values ( ⁇ ) and corresponding 0.05-0.95 inter percentile were 581 (368 - 788) and 552 (363 - 770), for colorimetric and fluorometric methods, respectively.
• Figure 6 shows weights for selected metabolites to be included in the model to predict energy balance. Metabolites were weighted at each week via the absolute value of the estimates generated from the regression model in Figure 9. For each metabolite, the weight at each week was adjusted to a % relative to 100,
• Figure 7 shows weekly Pearson's correlations between degree of physiological imbalance and calculated energy balance, milk yield, energy intake, or plasma NEFA, BHBA and glucose for cows during the periparturient period
• Figure 8 shows frequency distributions for cows that did or did not develop disease during wk 1 (WEEK1; i.e. 0 - 7 DIM) or during wk 2 through 9 of lactation (EARLYLACT)
• Figure 9 shows regression coefficient estimates, standard error and the probability that regression coefficient deviates from 0 for NEFA, glucose and BHBA as well as information for the regression model selected to predict degree of physiological imbalance based on between-cow variations in calculated EBAL from wk -4 to 9 relative to parturition.
• Figure 10 shows the least square means (LSM), SEM and the standardized differences (SDiff) for estimated between-cow variations in physiological imbalance (PI), calculated energy balance (EBAL), and plasma concentration of NEFA, BHBA and glucose at week -1 relative to development of metritis, retained placenta or milk fever during the first week after parturition.
• LSM least square means
• SDiff standardized differences
• Figure 11 shows the least square means (LSM), SEM and the standardized differences (SDiff) for estimated between-cow variations in physiological imbalance (PI), calculated energy balance (EBAL), and plasma concentration of NEFA, BHBA and glucose at week -1 relative to development of all diseases, lameness, or mastitis during either the first week after parturition.
• LSM least square means
• SDiff standardized differences
• PI physiological imbalance
• EBAL calculated energy balance
• plasma concentration of NEFA, BHBA and glucose at week -1 relative to development of all diseases, lameness, or mastitis during either the first week after parturition.
• Figure 14 shows results from Project 3 regarding differences in concentrations of plasma glucose (A), plasma non-esterified fatty acids (NEFA; B), plasma beta- hydroxybutyrate (BHBA; C), daily milk yield (D), milk BHBA (E), milk glucose (F), calculated energy balance ([15]; G), and liver triacylglycerol (TAG; H) at time points relative to nutrient restriction (0 - 96 h) from 12 Holstein cows in early (Early; ⁇ ) and mid-(Mid; ⁇ ) lactation classified as having either the least (Normal; solid lines) or greatest (Severe; dashed lines) degree of physiological imbalance based on an index generated from plasma NEFA, BHBA, and glucose.
• NEFA blood non-esterified fatty acids
• BHBA beta-hydroxybutyrate
• C glucose
• TAG liver triacylglycerol
• FIG 16 shows a flow diagram showing the preferred embodiment of the present invention and the general design. The present invention will now be described in more detail in the following. Detailed description of the invention
• the prior patent disclosed systems and methods for observing and prediction and physiological state of an animal.
• body fluids of animals in particular milk, urine and blood have been analyzed in order to obtain values of parameters, such as cell count in milk, lactate dehydrogenase (LDH), N-Acetyl-[beta]-D-glucosaminidase (NAGase), ketone bodies such as acetoacetate, beta-hydroxybutyrate (BHBA) and acetone, urea content, progesterone, or others, each of which by itself or in combination with others indicates a certain physiological state.
• LDH lactate dehydrogenase
• NAGase N-Acetyl-[beta]-D-glucosaminidase
• ketone bodies such as acetoacetate, beta-hydroxybutyrate (BHBA) and acetone, urea content, progesterone, or others, each of which by itself or in combination with others indicates a certain physiological state.
• concentration usually indicates mastitis, whereas the progesterone content is used for estrus detection, pregnancy or cysts depending on concentration and changes over time.
• animal characteristics characteristics that can not be changed with time
• characteristics of the animal such as e.g. genus, species, breed (e.g. cattle breed), genotype, and sex.
• physiological state characteristics that can change with time
• reproductive status such as non-estrus vs. estrus, non-pregnant (NP) vs. pregnant (P)
• productive status such as non-lactating (NL) vs. lactating (L)
• production relative to production capacity e.g. in relation to maximum genetic potential
• environmental factors should be understood as a broad term covering e.g. production system, season, diet, regional or national population differences.
• physiological imbalance should be understood as situation where one or more parameters of an animal of certain animal characteristics deviate from the normal at a given physiological state, and who consequently have an increased risk of diseases (clinical or subclinical), reduced production and/or reduced reproduction (Ingvartsen, 2006; Ingvartsen and Friggens, 2005).
• a "deviation from the normal” should be understood as e.g. concentrations of parameters in blood that are above or below the confidence limit (e.g. 70%) at given physiological state in healthy lactating animals of given animal
• the inventors of the present invention have realized that bringing animals in physiological imbalance in balance can prevent them from developing clinical or sub-clinical diseases. Hence, contrary to prior art, the present invention focuses on early identification and prevention of physiological imbalance that can thereby severely reduce the number of animals developing clinical or sub-clinical diseases, rather than detecting clinical disease after it has already happened, and acting subsequently to that.
• the inventors of the present invention have identified specific indicators of physiological imbalance, making it possible to estimate the risk of or degree of physiological imbalance.
• one aspect of the invention relates to a method for predicting the risk of physiological imbalance in an animal, said method comprises the steps of:
• the first risk indicator may be selected from the group consisting of free glucose, glucose-6-phosphate, glucose-l-phosphate, free galactose, UDP-glucose, UDP-galactose, glycerol 3-phosphate, beta- hydroxybutyric acid (BHBA), isocitrate, citrate, malate, 2-oxo-glutarate, isocitrate dehydrogenase, malate dehydrogenase, pyruvate carboxylase, glycogen phosphorylase, glutamate dehydrogenase, glyceraldehyde 3- phosphate dehydrogenase, aspartate aminotransferase, aldehyde dehydrogenase, phosphoglucomutase, triosephosphate isomerase, lactate, cholesterol, non-esterified fatty acids (NEFA), milk protein, milk fat, and milk lactose; (i)
• a risk of physiological imbalance based on combinations of risk indicators may in certain circumstances be parameterized to describe physiological imbalance better than any individual risk indicator.
• the method further comprises the steps of: (v) determining the concentration or amount of at least one second risk indicator present in the sample, the at least one second risk indicator, is different from the first risk indicator and may be selected from the group consisting of free glucose, glucose-6-phosphate, glucose-l-phosphate, free galactose, UDP-glucose, UDP-galactose, glycerol 3-phosphate, beta- hydroxybutyric acid (BHBA), isocitrate, citrate, malate, 2-oxo-glutarate, isocitrate dehydrogenase, malate dehydrogenase, pyruvate carboxylase, glycogen phosphorylase, glutamate dehydrogenase, glyceraldehyde 3- phosphate dehydrogenase, aspartate aminotransferase, aldehyde dehydrogenase, phosphoglucomutase, triosephosphate isomerase, lac
• the prediction based on the one or more risk indicator, indicates a risk of physiological imbalance in the animal, further comprising the step of:
• the variation in the risk indicator may be influenced by systematic factors such as e.g. animal characteristics, physiological state, and environmental factors.
• the risk of physiological imbalance in an animal may be determined from a comparison of a pattern in measured parameters, i.e. sample values, and a reference pattern (or a reference parameter value) which is typical for animals with certain animal characteristics.
• the one or more reference patterns are based on different physiological states.
• the one or more reference patterns are based on the specific animal characteristics.
• the one or more reference patterns are based on environmental factors
• the variation in the risk indicators may also be influenced by variation within a particular embodiment listed above (e.g. within breed).
• the one or more reference patterns are based on previous data obtained from the same animal.
• the animal is selected from the group of animal species consisting of ruminants. In one embodiment of the present invention, the animal is selected from the group of animal species consisting of non-ruminants.
• the body fluid is selected from the group consisting of milk, blood and urine.
• Non-limiting examples of parameters from the gluconeogenesis/glycolysis pathway are: glucose-6-phosphate, glucose-l-phosphate, glycerate 3-phosphate, lactose, UDP- glucose, and UDP-galactose.
• isocitrate being a risk indicator for physiological imbalance
• other parameters metabolites or enzymes
• TCA Cycle/Krebs Cycle or exports from the TCA-cycle (e.g. citrate) may also function as such.
• Non-limiting examples of parameters from the Citric Acid Cycle are: malate, isocitrate dehydrogenase, malate dehydrogenase, and pyruvate carboxylase.
• TAG glycol
• NEFA fatty acid oxidation
• cholesterol biosynthesis cholesterol
• protein breakdown/synthesis glutamate dehydrogenase and milk protein
• liver damage aspartate aminotransferase and aldehyde dehydrogenase
• NEFA Fatty acids
• lipolysis i.e. breakdown of fat
• lipogenesis i.e. synthesis of fat
• NEFA are released from fat tissue TAG, transported through the bloodstream, and are used as an energy source by many tissues when glucose is limited.
• NEFA In the liver, NEFA have 3 fates depending on energy needs, hormone balance, and substrate availability and include 1) complete breakdown with the release of energy via the tricarboxylic acid (TCA) cycle, 2) re-synthesized as TAG and stored within the liver or the newly synthesized TAG are exported into blood to other tissues, or 3) during periods of low blood glucose, breakdown of NEFA is incomplete and NEFA are then converted into ketone bodies (i.e. BHBA) which are released from the liver to the blood and can be used as an alternate energy source by many tissues (e.g. kidney, heart, and skeletal muscle) when glucose is low. Concentrations of blood NEFA and BHBA are associated with multiple diseases.
• TCA tricarboxylic acid
• BHBA ketone bodies
• an index of physiological imbalance based on a couple or several parameters (e.g. plasma NEFA, BHBA, glucose and cholesterol) and/or ratios between parameters, as well as calculated or predicted energy balance, body weight and weight changes, and body condition score or changes in body condition score may be more useful as indicators for degree of physiological imbalance that will more accurately reflect e.g. the overall metabolic status of cows throughout lactation than using changes in individual parameters alone.
• parameters e.g. plasma NEFA, BHBA, glucose and cholesterol
• glucose, BHBA, and NEFA have been identified as risk biomarkers in blood and have been used to generate an index for degree of physiological imbalance.
• these risk indicators are preferably measured in the milk, since samples can be taken automatically during milking in a non-invasive manner.
• the inventors have shown that the levels of glucose and BHBA in blood and milk are correlated.
• free glucose and isocitrate in milk are potential biomarkers for degree of physiological imbalance for cows during lactation.
• the risk of physiological imbalance is predicted by measuring free glucose as the first risk indicator. In another embodiment of the present invention, the risk of physiological imbalance is further predicted by measuring isocitrate as the second risk indicator.
• the animal is a cow predicted in physiological imbalance based or a first and/or second indicator, and the health status such as risk of sub-clinical or clinical disease ketosis is identified by measuring BHBA as a the second or the third risk indicator.
• the models may have 4 or more major outputs: 1) an overall risk of physiological imbalance presented to the user; 2) a calculation or indication of when to take the next sample that feeds back to the analysis apparatus; 3) in the case of high risk of physiological imbalance (PI; i.e. risk of PI > default risk limit), additional analysis of disease indicators to determine the risk of a disease state, e.g. sub-clinical or clinical ketosis; and 4) in the case of low risk of physiological imbalance (i.e. risk of PI ⁇ default risk limit), additional analysis for e.g. measurements of reproductive status (i.e. R n ; i.e. estrus vs. non-estrus).
• Two modules generate these outputs: 1) using the information provided by the signal(s), i.e. PIi and, optionally, PI 2 , coming from the analysing apparatus and other additional risk factors (e.g. day in milk, body weight, and energy balance) for generation of risk of physiological imbalance (i.e. output 1); and 2) using the risk assessment generated by output 1 coupled with additional factors to determine risk of clinical or sub-clinical disease such as e.g. ketosis (i.e. output 2) or e.g. reproductive status (i.e. output 3).
• This structural separation is designed to optimize analyses that reduce false positives for disease and improve reproductive conception rates.
• Another aspect of the present invention relates to a system for predicting the risk of physiological imbalance in an animal, the system comprising :
• a computer comprising a processor and being operatively connected to a
• At least one sample providing device for repetitively providing at least one sample of a body fluid of the animal
• the first risk indicator may be selected from the group consisting of free glucose, glucose-6- phosphate, glucose-l-phosphate, free galactose, UDP-glucose, UDP galactose, glycerol 3-phosphate, beta-hydroxybutyric acid (BHBA), isocitrate, citrate, malate, lactate, 2-oxo-glutarate, cholesterol, non esterified fatty acids (NEFA), isocitrate dehydrogenase, malate dehydrogenase, pyruvate carboxylase, glycogen phosphorylase, glutamate dehydrogenase, glyceraldehyde 3-phosphate
• BHBA beta-hydroxybutyric acid
• NEFA non esterified fatty acids
• isocitrate dehydrogenase malate dehydrogenase
• pyruvate carboxylase glycogen phosphorylase
• glutamate dehydrogenase glyceraldehyde 3-phosphate
• dehydrogenase phosphoglucomutase
• triosephosphate isomerase milk protein, milk fat, and milk lactose
• (A2) optionally, determining the concentration or amount of at least one second risk indicator present in the sample, the at least one second risk indicator, is different from the first risk indicator and may be selected from the group consisting of free glucose, glucose- 6-phosphate, glucose-l-phosphate, free galactose, UDP-glucose, UDP-galactose, glycerol 3-phosphate, beta-hydroxybutyric acid (BHBA), isocitrate, citrate, malate, lactate, 2-oxo-glutarate, cholesterol, non-esterified fatty acids (NEFA), isocitrate
• BHBA beta-hydroxybutyric acid
• isocitrate citrate
• malate lactate
• 2-oxo-glutarate cholesterol
• NEFA non-esterified fatty acids
• dehydrogenase malate dehydrogenase, pyruvate carboxylase, glycogen phosphorylase, glutamate dehydrogenase, glyceraldehyde 3-phosphate dehydrogenase, aspartate aminotransferase, aldehyde dehydrogenase, phosphoglucomutase, triosephosphate isomerase, milk protein, milk fat, and milk lactose; a data interface for repetitively entering the concentration or amount of the first risk indicator and, optionally, the at least one second risk indicator in the database, wherein the database is adapted to store multiple database entries representing the indicator at various points in time, and wherein the processor is programmed to:
• PI determine the rate of change of the first indicator and, optionally, the at least one second indicator
• P2 determine the level change of the first indicator and, optionally, the at least one second indicator and,
• P3 compare said rate change and/or level change with one or more reference patterns of said first risk indicator and, optionally, the at least one second risk indicator, in order to predict the degree of or risk of physiological imbalance in the animal.
• the processor is further programmed to:
• the processor is further programmed to:
• the processor is further programmed to:
• the processor is further programmed to:
• the analysis apparatus is further capable of:
• the processor is further programmed to:
• (P7a) compare the rate change and/or level of change of the at least one disease indicator with one or more reference patterns of said at least one disease indicator, in order to predict the health status, e.g. the risk of the animal entering into a sub-clinical or clinical disease state.
• the analysis apparatus is further capable of:
• A3b determining the concentration or amount of at least one indicator (e.g. progesterone) of reproductive status present in the sample.
• at least one indicator e.g. progesterone
• the processor is further programmed to:
• (P5b) determine the rate of change of the at least one indicator (e.g. progesterone) of reproductive status;
• (P6b) determine the level change of the at least one indicator (e.g. progesterone) of reproductive status;
• P7b) compare the rate change and/or level of change of the at least one indicator (e.g. progesterone) of reproductive status with one or more reference patterns of said at least one indicator of reproductive status, in order to predict the reproductive status.
• the term "univariate data analysis” refers to data analysis in which data relating to a single variable are analysed.
• the univariate data analysis may comprise analysis of correlated univariate variables.
• multivariate data analysis refers to data analysis in which data relating to at least two variables are analysed.
• a result from the univariate or multivariate analysis may be used as an input for further analysis.
• the further analysis may be univariate or multivariate.
• PCA Principal Component Analysis
• SSM State Space Model
• the processor is programmed to: perform at least one mathematical analysis of the at least one sample value, and compare the at least one mathematical analysis with a pattern in measured parameters in order to select, the point in time for providing a subsequent sample and performing a subsequent analysis of said subsequent sample for at least one of the parameters.
• the mathematical analysis is a statistical analysis.
• the statistical analysis is a univariate analysis of the database entries to obtain a first set of data representing expected sample values of at least one of the parameters at future points in time.
• the statistical analysis is a multivariate analysis of the database entries to produce a second set of data derived from combined analysis of sample values of at least two parameters.
• the inventors have realized that the concentration (molar) in milk of free glucose and glucose-6-phosphate can be important markers for physiological imbalance in a mammal.
• the concentrations in milk of free glucose and glucose-6-phosphate are not linearly related both under normal conditions and under physiological imbalance. Hence, it is important to test the two substrates independently of each other to obtain as much information as possible.
• the method further comprises the step of combining the indicator based risk with one or more additional risk factors in order to predict the risk of physiological imbalance.
• Such combination may be performed as the sum of indicator based risk of physiological imbalance and additional risk factor based risk.
• multivariate determined risk of physiological imbalance based on measured indicators and additional risk factors can be performed (see Figure 16). Additional risk factor based risk may be predicted using univariate biomodels or multivariate approaches.
• the processor in the system is programmed to combine the indicator based risk with one or more additional risk factors in order to predict the risk of physiological imbalance.
• the Additional Risk Factor (ARF) is The Additional Risk Factor (ARF)
• the crucial point about the Additional Risk Factor is that the risk of physiological imbalance included here are not already included in the Indicator Based Risk, i.e. any factor which can be measured in the body fluid of the animal should not be included here.
• the elements that make up the ARF are described below, they combine to give ARF.
• Milk Yield (MY) (kg/day).
• MYAcc is a way of combining milk yield and days from calving which we believe crystallises the components of these two factors which are relevant to the physiological stress being experienced by the cow. MYAcc is highest immediately after calving and is higher for higher yielding cows.
• the aim of the present invention is to provide a method for determining the concentration of free glucose in a liquid comprising glucose and glucose-6-phosphate, the method comprising the steps of:
• NAD(P) thereby producing 6-phosphogluconate and NAD(P)H2, wherein n is a natural integer of at least 1;
• n is a natural integer of at least 1; 5) bringing into contact with at least one liquid sample B n from a liquid sample M at least one type of glucose-6-phosphate-dehydrogenase and NAD(P), thereby producing 6-phosphogluconate and NAD(P)H2;
• the aim of the present invention is to provide a dry stick test device for the determination of free glucose in a liquid sample comprising free glucose and glucose-6-phosphate (such as a milk sample) by means of a chemical assay, wherein said dry stick device is constructed in such a manner so as to determine the amount of both glucose-6-phosphate and free glucose in a liquid sample.
• the dry stick test device comprises: (i) optionally a solid support,
• At least one reagent pad A comprising at least one type of glucose-6- phosphate-dehydrogenase, NAD(P), at least one type of NAD(P)H dehydrogenase and a fluorophore/chromophore precursor,
• At least one reagent pad B comprising at least one type of glucose-6- phosphate-dehydrogenase, NAD(P), at least one type of NAD(P)H dehydrogenase, at least one type of hexokinase, adenosine triphosphate (ATP) and a
• Nicotinamide adenine dinucleotide is a coenzyme.
• the compound is a dinucleotide, since it consists of two nucleotides joined through their phosphate groups.
• One nucleotide contains an adenine base and the other nicotinamide.
• NADP Nicotinamide adenine dinucleotide phosphate
• NAD Nicotinamide adenine dinucleotide phosphate
• NAD(P) means NAD and/or NADP.
• NAD(P)H dehydrogenase means NADH dehydrogenase (EC 1.6.99.3) and/or NADPH dehydrogenase (EC 1.6.99.1).
• the glucose-6-phosphate-dehydrogenase according to the present invention is classified in IUBMB Enzyme Nomenclature as 1.1.1.49.
• the hexokinase according to the present invention is classified in IUBMB Enzyme Nomenclature as 2.7.1.1.
• a fluorophore or fluorochrome, similarly to a chromophore is a fluorescent chemical compound that can re-emit light upon light excitation.
• a chromophore is the part of a molecule responsible for its color. The color arises when a molecule absorbs certain wavelengths of visible light and transmits or reflects others.
• a fluorophore/chromophore precursor is to be understood as a molecule that upon reduction with at least one type of NAD(P)H produces a fluorophore/chromophore.
• fluorophore/chromophore precursors suitable in each specific assay may be easily recognised by the person skilled in the art and may be introduced into a dry stick test device according to the present invention.
• the fluorophore/chromophore precursor is selected from the group consisting of a tetrazolium salt; 4- aminoantipyrine/3,5-dimethoxy-N-ethyl-N-(2-hydroxy-3-sulfopropyl)-aniline sodium salt; 4-aminoantipyrine/l-naphthol-3,6-disulfonic acid-2-sodium salt; 4- aminoantipyrine/N-ethyl-N-(2-hydroxy-3-sulfopropyl)-m-toluidine sodium salt; 4- aminoantipyrine/l,7-dihydroxynaphthalene; 4-aminoantipyrine/3,5-dichloro-2- hydroxybenzene sulfonate; Tetrazolium violet; 3,5
• Bromocresol green Bromophenol blue; Arsenazo III; 2-(3,5-dimethoxy-4- hydroxyphenol)-4,5-bis(4-dimethylaminophenyl)-imidazole; Pyridylazo dye;
• Magenta coupler dye l,5-bis(2-hydroxy-3,5-dichlorophenyl)-3-cyano formazan; Copper tartrate; 3-methyl-2-benzothiazolinone hydrazone; N-propyl-4-(2,6- dinitro-4-chlorobenzyl)quinolinium ethane sulfonate; Hydroxydiaryl imidazole; 2- methoxy-4-morpholinophenyl diazonium tetrachlorozincate; 3,3',5,5'- tetramethylbenzidine; 4-aminophenazone/3,5-dichloro-2-hydroxybenzene sulfonate; Primaquine diphosphate/3-methyl-2-benzothiazoline hydrazone; 2,5- dinitrobenzoic acid; 2-(p-indophenyl)-3-(p-nitrophenyl)-5-phenyltetrazolium chloride; 3-hydroxy-l,2,3,4-tetrahydrobenzo-(
• a sample relates to any sample found in the form of liquid, solid or gas and which may be liquefied at the time of assaying.
• a liquid sample may be applied.
• handling steps relates to any kind of pre-treatment of the liquid sample before or after it has been applied to the assay device.
• This pre- treatment comprises separation, filtration, dilution, distillation, concentration, inactivation of interfering compounds, centrifugation, heating, fixation, addition of reagents, or chemical treatment.
• the sample may be collected from a mammal, preferably the mammal is selected from the group consisting of herd animals, cows, camels, buffaloes, pigs, horses, deer, sheep, goats, pets, dogs, cats and humans.
• the sample can be derived from any desirable source, however, it is preferred that the sample is selected from the group consisting of milk, blood, serum, plasma, saliva, urine, sweat, ocular lens fluid, cerebral spinal fluid, ascites fluid, mucous fluid, synovial fluid, peritoneal fluid, amniotic fluid or the like.
• physiological fluids other liquid samples such as various water samples, food products and the like can be used.
• a solid test sample can be used once it is modified to form a liquid sample, for instance in the form of a solution, a suspension or an emulsion.
• the inventors of the present invention have shown that oxamate inhibited unspecific production of NAD(P)H most likely originating from the action of lactate dehydrogenase (LDH, EC 1.1.1.27).
• the reagent pad A and/or B further comprise an inhibitor of lactate dehydrogenase.
• Oxamate is a well-known competitive inhibitor of LDH (Larsen, 2005) and it has been used formerly in comparable assays to suppress LDH activity (Larsen & Nielsen, 2005).
• the reagent pad A and/or B further comprise oxamate.
• the dry stick test device further comprises a development pad.
• the at least one reagent pad A and/or B provide a first environment for said reagent(s), said first environment permitting an improved storage stability of the reagent(s) and dry stick device when in a non-moistened state, the dry stick device further comprising a regulating pad being in contact with the at least one reagent pad A and/or B, wherein the regulating pad creating a second environment for said reagent(s) when in a moistened state, said second environment permitting an increased rate of reaction between the analyte and the reagent(s), and wherein the condition in the first environment is provided by adjusting the pH-value to a value that deviates from the optimal pH-value of the enzyme(s) and wherein the condition in the second environment is provided by regulating the pH-value to a value that approaches the optimal pH-value of the enzyme(s) and wherein the different environments have different pH-values.
• One aspect relates to the use of a dry stick device according to the present invention for the determination of free glucose and glucose-6-phosphate in a milk sample. Preparation of the dry stick
• the dry stick device according to the present invention may be prepared by any conventional methods provided for the preparation of dry stick devices.
• the method for providing a dry stick device according to the present invention comprises the steps of:
• reagent pad A by impregnating a first porous material with an aqueous solution comprising at least one type of glucose-6- phosphate-dehydrogenase, NAD(P), at least one type of NAD(P)H dehydrogenase and a fluorophore/chromophore precursor, the at least one reagent pad providing a first environment for said reagent(s),
• reagent pad B by impregnating a first porous material with an aqueous solution comprising at least one type of glucose-6-phosphate-dehydrogenase, NAD(P), at least one type of NAD(P)H dehydrogenase, at least one type of hexokinase, adenosine triphosphate (ATP) and a fluorophore/chromophore precursor, the at least one reagent pad providing a first environment for said reagent(s),
• the at least one reagent pad and the regulating pad may be contacted by substantially fully overlapping the pads, by partial overlap of the pads or by laying the regulating pad adjacent to at least one reagent pad.
• the arrangement of the pads may be selected in such a manner to avoid precipitation of a sample component on the top face of the device.
• the sample components that may precipitate can be selected from the group consisting of proteins, carbohydrate, fat, cells, or other component present in the sample.
• the first environment may be selected in such a manner as to favour the storage of the reagent(s) capable of reacting with the analyte and providing a detectable signal - as described earlier.
• the second environment may be selected in such a manner as to favour the performance of the reagent(s) capable of reacting with the analyte and providing a detectable signal - also as described earlier.
• the second environment may be selected in such a manner as to favour the rate of reaction between the analyte and the reagent(s) capable of reacting with the analyte providing a detectable signal - as described earlier.
• the device according to the present invention may be supported by a solid support.
• solid support refers to a material, which has no influence on the migration or on the reaction of the liquid sample or on reagent(s) or the agents capable of increasing the rate of the reaction.
• the solid support provides a stabilising basis for the assay device and provides sufficient strength to maintain the desired physical shape and has substantially no interference with the production of a detectable signal.
• the material for the solid support is selected from the group consisting of tubes, polymeric beads, nitrocellulose strips, membranes, filters, plastic sheets and the like.
• polysaccharides for instance cellulosic materials such as paper and cellulosic derivatives, such as cellulose acetate and nitrocellulose, silica- orinorganic materials, such as, for example, deactivated alumina, diatomaceous earth, MgS0 4 or other inorganic finely divided material uniformly dispersed in a porous polymeric matrix, wherein the matrix may comprise one or more polymers such as homopolymers and copolymers of vinyl chloride, for instance, polyvinyl chloride, vinyl chloride-propylene copolymer, and vinyl chloride-vinyl acetate copolymer, cloth, both naturally occurring (for instance, cotton) and synthetic (for instance, nylon), porous gels, such as silica gel, agarose, dextran, and gelatin, polymeric films, such as polyacrylamide, and the like.
• cellulosic materials such as paper and cellulosic derivatives, such as cellulose acetate and nitrocellulose
• the solid support may be omitted from the dry stick test device.
• the dry stick test device comprises at least one reagent pad and, optionally, a development pad.
• the sample may by applied to the dry stick test device on one surface and the detectable signal may be detected on the same or another surface, thus it is preferred that any possible precipitation of sample components on the surface where the detectable signal are to be detected may be limited or avoided.
• reagent pad relates to one or more pads comprising a reagent or a combination of reagents.
• combination of reagents may preferably be impregnated into the reagent pad in such a manner that the reagent or the combination of reagents is/are immobilised when in dry state and mobile when in moistened state.
• reagent relates to the chemical substance that reacts with or participate in or is necessary for the determination of an analyte, a derivative of said analyte or an indicator compound for said analyte to provide a detectable signal.
• a similar definition of the combination of reagents may be provided which relates more specifically to 2 or more reagents, such as 3 or more reagents, e.g. 4 or more reagents, such as 5 or more reagents, e.g. 6 or more reagents.
• the dry stick test device comprises at least 2 reagent pads, such as at least 3 reagent pads, e.g. at least 4 reagent pads, such as at least 5 reagent pads, e.g. at least 6 reagent pads.
• the reagents that reacts with or participate in or is necessary for the determination of an analyte, a derivative of said analyte or an indicator compound for said analyte to provide a detectable signal may be introduced into different reagent pads. This may improve stability, storage properties and applicability of the dry stick device because non-compatible compounds can be included in different reagent pads of the dry stick device.
• development pad relates to a pad capable of regulating the environment and the conditions for the sample comprising the analyte to an environment that facilitates the determination of the analyte, a derivative of said analyte or an indicator compound for said analyte.
• the development pad may comprise one or more controlling compounds capable of increasing the rate of the reaction between the analyte, a derivative of said analyte or an indicator compound for said analyte present in the sample and the reagent(s).
• the controlling agent may be an acid or a base.
• the development pad is in contact with at least one reagent pad by substantially fully overlapping, by partial overlap or by laying adjacent to at least one reagent pad.
• the development pad is overlapping the at least one reagent pad by at least 5%, such as at least 10%, e.g. at least 25%, such as at least 50%, e.g. at least 75%, such as at least 80%, e.g. at least 90%, such as at least 95%.
• substantially fully overlapping relates to two separate pads (the regulating pad and the at least one reagent pad) being placed on top of one another.
• partial overlap relates to two separate pads (the regulating pad and the at least one reagent pad) being overlapping with only part of the pad(s).
• a partial overlap of 100% relates to a full overlap and a deviation of 5% from the 100% full overlap relates to a substantially full overlap.
• the development pad and the at least one reagent pad(s) are laying adjacent to one another. This means that the pads are placed in contact with each other (touching each other).
• An overlap of 0% (but in contact) relates to the term "laying adjacent", furthermore, an overlap of less than 5% is considered being within the term of "laying adjacent”, such as an overlap of at the most 4%, e.g. an overlap of the most 3%, such as an overlap of the most 2% or e.g. an overlap of the most 1%.
• controlling compound In the development pad a controlling compound is immobilised.
• controlling compound relates to a substance that has the function as a propellant or a fuel in the specific assay for the determination of the analyte (free glucose and/or glucose-6-phosphate), a derivative of said analyte or an indicator compound for said analyte.
• the controlling compound may also be the chemical substance responsible for the precipitation of sample components or the chemical compound causes the sample components not to precipitate.
• the controlling compound may be separated from at least one of the reagents in order to improve the stability of the dry stick test device.
• the controlling compound may be an acidic or an alkaline compound.
• the controlling compound is an acidic compound capable of providing a pH-value of the sample in the dry stick test device, when in a moistened state, below pH 6, such as below pH 5, e.g. below pH 4, such as below pH 3, e.g. below pH 2, such as below pH 1, e.g. below pH 0, such as in the range of pH 0-6, e.g. in the range of pH 0-5, such as in the range of pH 0-4, e.g. in the range of pH 0-3, such as in the range of pH 0-2, e.g . in the range of pH 0- 1, such as in the range of pH 1-6, e.g. in the range of pH 2- 6, such as in the range of pH 3-6, e.g. in the range of pH 4-6, such as in the range of pH 5-6.
• the controlling compound may be an alkaline compound capable of providing a pH-value of the sample in the dry stick test device, when in a moistened state, of pH 8 or above, such as in the range of pH 8- 14, e.g. in the range of pH 8- 13, such as in the range of pH 8- 12, e.g. in the range of pH 8- 11, such as in the range of pH 8- 10, e.g. in the range of pH 8-9, such as in the range of pH 9- 12, e.g. in the range of pH 10- 13, such as in the range of pH 10- 11.
• an alkaline compound capable of providing a pH-value of the sample in the dry stick test device, when in a moistened state, of pH 8 or above, such as in the range of pH 8- 14, e.g. in the range of pH 8- 13, such as in the range of pH 8- 12, e.g. in the range of pH 8- 11, such as in the range of pH 8- 10, e.g. in the range of pH 8-9, such as
• Glucose and glucose 6- phosphate seems to be negatively and positively correlated, respectively, to the well established indicator of ketosis, beta-hydroxy butyrate in milk, indicating an auspicious future for milk glucose and glucose 6-phosphate as indicators of animal physiological status.
• the present analytical procedure facilitates further
• index (xl x [NEFA]) + x2 x
• liver TAG is highly correlated to the
• Results identified 4 proteins as potential indicators of physiological imbalance (glutamate dehydrogenase, malate dehydrogenase, glyceraldehyde 3-phospate dehydrogenase, and phosphoglucomutase).
• Project 6 Biomarkers in milk of physiological imbalance.
• Cows with a greater degree of physiological imbalance experienced higher isocitrate and lower free glucose in milk when compared to cows with lower degree of physiological imbalance.
• Breed and stage of lactation altered concentrations of free glucose and isocitrate; and free glucose in milk was affected by parity. Results will be implemented in future biomodels for in-line and real-time measurement of degree of physiological imbalance on-farm .
• the objective is to describe a new enzymatic-fluorometric method for
• the objective for Project 2 was to generate an index for PI based on several plasma metabolites and to compare the use of this index with calculated energy balance (EBAL) and individual plasma metabolites in relation to risk of disease during early lactation.
• the project comprises the following main activities:
• the objective for Project 3 was to identify and validate risk indicators for monitoring physiological imbalance and individual differences in the response of cows to changes in the nutrient supply.
• the project comprises the following main activities: a) characterizing the changes in milk components during nutrient restriction for identification of potential risk indicators for physiological imbalance;
• the aim of Project 4 was to describe the liver proteome in early and mid-lactation for cows at different degrees of physiological imbalance with a special focus on biomarkers and pathways involved in periparturient disease complexes.
• the project comprises the following main activities: a) identification of risk biomarkers for physiological imbalance in liver biopsies by means of quantitative proteome analysis (LC-MS/MS); b) applied screening for relevant liver risk indicators in more accessible samples (milk/blood/urine) via combined mass spectrometry and clinical-chemical analyses with a view to describing the practical application and value of the risk indicators.
• Project 5 objective was to describe the liver proteome in early and mid-lactation for cows at different degrees of physiological imbalance with a special focus on biomarkers and pathways involved in periparturient disease complexes.
• the project comprises the following main activities: a) identification of risk biomarkers for physiological imbalance in liver biopsies by means of quantitative proteome analysis (LC-MS/MS); b) applied screening for relevant liver risk indicators in more accessible samples (milk/
• the objective for Project 5 was to identify and validate risk indicators for monitoring physiological imbalance in liver samples from cows 1 week after parturition (i.e. high risk period for physiological imbalance) by means of quantitative proteome analysis (LC-MS/MS).
• the project comprises the following main activities: a) identification of risk biomarkers for physiological imbalance in liver biopsies by means of quantitative proteome analysis (LC-MS/MS);
• Project 6 objective The objective of was to identify potential biomarkers in milk that relate to degree of PI as an early warning system for risk of disease on-farm and to quantify the variation in the biomarkers of interest with regard to systemic effects i.e. breed, parity and stage of lactation and across different production systems.
• the project comprises the following main activites:
• Glucose 6-phosphate was determined separately by enzymatic oxidation by glucose 6-P dehydrogenase using NADP + dependent enzyme from Saccharomyces sp. (EC 1.1.1.49; Roche 10 127 655 001). The sum of free glucose and glucose 6- phosphate (henceforth denoted total glucose) was determined by enzymatic oxidation by hexokinase (EC 2.7.1.1; Roche 11 426 362 001) and glucose 6- phosphate dehydrogenase from Leuconostoc sp. (cofactor NAD + and NADP + ; EC 1.1.1.49; Roche 10 165 875 001). Free glucose was consequently estimated as the difference between the two results.
• the enzymatic-fluorometric method of total glucose determination is a three step enzymatic procedure to obtain the fluorescent product equivalent to the glucose content.
• the two first steps are identical to the widely used
• the last step is an enzymatic coupling of the reducing equivalents from NAD(P)H to the non- fluorescent compound resazurin mediated by the enzyme diaphorase (EC 1.6.99.- _). Resazurin is reduced by NAD(P)H and the highly fluorescent substance resorufin is developed and measured fluorometrically (Larsen and Nielsen, 2005).
• Reducing equivalents (NADPH) from the separate glucose 6-phosphate oxidation is in the same way coupled to resazurin in order to quantify glucose 6-phosphate fluorometrically.
• Reagents, combined glucose and glucose 6-phosphate analyses Reagent 1. Tris-buffer, 100 mM, pH 7.6 with 10 mM Mg ++ , 3.6 mM Na- oxamate (MW 111.03), 1.9 mM ATP (Na 2 -salt, MW 551.2) and 1.9 mM NAD (Na 2 - salt, MW 717.5).
• hexokinase enzyme and glucose-6-phosphatase (Roche 10 737 275 001), 3.1 U and 1.6 U/ml, respectively.
• Tris-buffer 60 mM, pH 7.2, with 1.28 mM resazurin (Sigma R- 2127, MW 251.2), 0.01% Triton X-100, and 9.8 U/ml diaphorase (Toyobo
• Reagent 1 Tris-buffer, 60 mM, pH 7.2 with 3.6 mM Na-oxamate and 1.9 mM NADP + (di-Na-salt, MW 787.4). Immediately before use the solution was supplied with glucose 6-phosphate dehydrogenase enzyme 1.6 U/ml.
• Each plate contained 2 * 8 standard solutions (glucose) and 2 * 4 control solutions.
• the samples were read against a standard curve; control samples worked as internal control and day to day check.
• Standards and control samples were prepared (independently) from glucose monohydrate (MW 198.2) and glucose 6-P (MW 304.2), respectively (stored in excicator), and water.
• Standard concentrations used were 0; 0.24; 0.48; 0.72; 0.96; 1.20; 1.80; and 2.40 mM (combined glucose and glucose 6-phosphate) and 0; 0.08; 0.16; 0.24; 0.32; 0.48; 0.64; and 0.80 mM (glucose 6-phosphate).
• Control samples were 0.5; 1.0; 1.5; and 2.0 mM (combined glucose and glucose 6-phosphate) and 0.15; 0.30; 0.45; and 0.75 mM (glucose 6-phosphate), respectively.
• Total glucose 144 milk samples were replicated three times within the same plate and 72 samples were replicated three times between plates.
• Glucose 6-phosphate 72 milk samples were replicated three times within the same plate and 72 samples were replicated three times between plates.
• the absorbance was compared with a standard curve (0 - 0.8 mM).
• the spectrophotometric analyses were performed using an autoanalyzer (ADVIA 1650, Siemens Diagnostics ® ). Intra-assay precision and accuracy (bias %) were both within 2% for samples and control samples,
• milk somatic cell counts SCC.
• the milking system is a robotic system, where representative milk samples are taken in a 10 ml tube, pre-dosed with the preservative Bronopol ® to obtain 100 mg/kg sample. The samples were stored at 4 °C and brought to the laboratory every morning. The milk samples were analysed immediately upon arrival for a period of 2 months. Milk citrate, lactose, fat and protein were determined by IR-spectroscopy (CombiFoss 4000, Foss Electric Ltd., Hiller0d, DK).
• a subset of cows from Project 3 in early and mid-lactation were used to calculate a physiological imbalance index using average plasma NEFA, BHBA and glucose concentrations from the period prior to nutrient restriction.
• index [In(NEFA)] + [In(BHBA)] - [glucose].
• index [In(NEFA)] + [In(BHBA)] - [glucose].
• n 3; severe
• n 3; normal
• the inventors have suggested including cholesterol as it is correlated with milk glucose as well as including calculated energy balance, body weight, and/or body condition score.
• the value of ratios to predict physiological imbalance and the identification of indicators from body fluids will also be assessed.
• the concentration of glucose 6-phosphate in milk was affected by breed of the animal (Danish Friesian vs. Jersey), parity and the interaction between these variables (p ⁇ 0.001), Figure 2. Free glucose was significantly affected by parity and the interaction between breed and parity (p ⁇ 0.001).
• the present enzymatic-fluorometric methods for determination of free glucose and glucose 6-phosphate in milk are reliable analytical methods.
• the determination of free glucose and glucose 6-phosphate responds linearly to standards, to the indigenous monosaccharide content of milk, and to spiking of milk samples with the respective monosaccharides.
• the precision of the assay is acceptable for analytic as well as descriptive use.
• the assays work without time or material consuming pre-treatment of the samples, and it is in this context a "high throughput" assay, i.e. hundreds of samples may be analysed daily in automated laboratories.
• Oxamate is a well-known competitive inhibitor of LDH (Larsen, 2005) and it has been used formerly in comparable assays to suppress LDH activity (Larsen & Nielsen, 2005).
• LDH is a well-documented indigenous enzyme in milk.
• the activity originates 5 mainly from somatic cells, leucocytes and invading microorganisms, and
• mastitic milk is associated with higher LDH activities (e.g. Chagunda et al, 2006 a; Chagunda et al, 2006 b; Friggens et al. 2007) just as endotoxins like E. coli lipopolysaccharides (LPS) infused in milk quarters increase the activity (Larsen et al, 2010). LDH will mediate the conversion of lactate to pyruvate
• L-lactate is present in non-mastitic milk (approx. 0.1 - 0.2 mM) and increases up to 30-fold during mastitis (SCC; Davis et al, 2004) or experimental inflammations (Silanikove et al,
• Lemosquet et al (2004) reported on average between 480 - 580 ⁇ ; Rigout et al (2002) between 430 - 570 ⁇ ; Hurtaud et al. (1998 and 2000) between 720 - 830 ⁇ and 510 - 650 ⁇ ,
• glucose 6-phosphate in milk are based on precipitation of protein and fat (perchloric acid precipitation) and subsequent colorimetric determination; and most studies find a concentration between 20 and 140 ⁇ (e.g. Hurtaud et al. 1998 and 2000; Rigout et al. 2002 and 2003; Faulkner 1980; Faulkner and Pollock, 1989) that are marginally lower than the present enzymatic- fluorometric determination but within the same range for a milk metabolite.
• the positive correlation between free glucose and lactose may reflect intra-cellular conditions; i.e. uptake of glucose from circulation, the utilization of glucose in lactose synthesis (various steps) and the secretion of lactose and glucose to the milk.
• the inverse correlation between glucose 6-phosphate and lactose may be related to lactose synthesis and milk production because glucose 6-phosphate is an intermediate in the pentose phosphate cycle, developing reducing equivalents (NADPH) used for reductive biosynthesis of milk fat especially.
• the measured milk variables in the present study show several interesting connections to glucose 6-phosphate and free glucose, e.g. positive correlations between the well-established indicator of ketosis, BOHB and glucose 6-phosphate and a negative correlation between BOHB and glucose.
• the correlations reveal a connection between the constituents and indicate that milk glucose and glucose 6- P could be important markers of physiological status together with other indicators in milk, although additional statistical analyses based on physiological considerations are needed.
• Faulkner et al. (1981) showed that milk glucose concentration in goats fell considerably during starvation. Faulkner and Pollock (1989) observed an increase in milk glucose when cows were fed unprotected soybean free fatty acids.
• Adjusting for fixed effects eliminates known factors that cause variation in the parameters of interest (i.e. blood NEFA, BHBA and glucose). This, in turn, reduces the correlation between EBAL and blood metabolites.
• An index for PI based on plasma NEFA, BHBA a nd glucose, related to risk for most diseases, i .e. metritis, m ilk fever, retained placenta, lameness and mastitis ( Figures 8, 10 and 11), and is therefore a better predictor for risk of disease during ea rly lactation than EBAL, glucose and BHBA.
• the study showed that prepa rta l deg ree of PI was a good pred ictor of d isease after calving and may potentially be related to hea lth problems that occurred in the previous lactation (Ingva rtsen, 2006) .
• Calculated EBAL failed to predict risk of all diseases during early lactation, except milk fever.
• hig her prepartum NEFA was a lso associated with incidence of diseases such as metritis, mastitis and RP.
• prepa rtal BHBA was not associated with the incidence of any disease after pa rturition .
• Our results indicate that prepa rta l NEFA are a better indicator of risk for disease tha n BHBA.
• NEFA explained the majority of the variation in physiological imbalance and suggests that changes in NEFA more accurately reflect metabolic status and risk of disease than BHBA.
• Nutrient restriction increased degree of physiological imbalance via increased plasma BHBA and NEFA and liver TAG and decreased glucose and liver glycogen content (Figure 12). Regardless of stage of lactation, greater changes in isocitrate (96%) and free glucose (-23%) concentration in milk were observed during nutrient restriction (0-96 h) when compared BHBA (8%) and glucose-6-phosphate (1%; Figure 13). In addition, greater changes in fat (36%) and milk yield (-32%) were observed during restriction. However, milk fat and milk yield have been associated with other diseases, such as mastitis (Moyes et al., 2009), and therefore, may lack specificity as general risk indicators for physiological imbalance. Furthermore, changes in free glucose and isocitrate in milk were the most rapid (i.e.
• analyses in blood and milk is expected to be possible for at least 4 enzymes (i.e. glutamate dehydrogenase, malate dehydrogenase, glyceraldehyde 3-phosphate dehydrogenase, and phosphoglucomutase) for use as potential risk biomarkers for physiological imbalance.
• enzymes i.e. glutamate dehydrogenase, malate dehydrogenase, glyceraldehyde 3-phosphate dehydrogenase, and phosphoglucomutase
• glycolysis/gluconeogenesis glycolysis/gluconeogenesis (glyceraldehyde 3-phoshate dehydrogenase), glycogen synthesis/degradation (phosphoglucomutase), and the TCA cycle (malate dehydrogenase).
• Results from all projects identify a potential coping mechanisms utilized by cows in severe physiological imbalance and identified pathways and specific parameters altered by physiological imbalance that provides information into new avenues linking physiological imbalance and risk of disease thereby improving animal health and productivity during lactation.
• This study provided evidence for the use of free glucose and isocitrate in milk as biomarkers for degree of physiological imbalance. Results will be implemented in future biomodels for in-line and real-time measurement of degree of physiological imbalance on-farm.
• Hurtaud, C, Rulquin, H., and Verite, R. (1998). Effect of graded duodenal infusions of glucose on yield and composition of milk from dairy cows. 1. Diets based on corn silage. Journal of Dairy Science, 81, 3239-3247.

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