Metabolic Profiling

A "metabolic profile" is a series of specific analytical tests run in combination and used as a herd-based, rather than individual-based, diagnostic aid.
Metabolic Profiling - Articles

Updated: September 7, 2017

In This Article
Metabolic Profiling

What is Metabolic Profiling?

Blood tests from individual animals are routinely used to diagnose disease problems in dairy cattle. Veterinarians, producers, and nutrition consultants alike seem interested in extracting pertinent information relative to herd nutrition and health status from blood testing. Relationships between nutritional status, metabolic state of the animal, and blood analyte (i.e., collective term for various nutrient and metabolic parameters measured) concentrations have been well documented in experimental research under controlled conditions. Properly applied metabolic profile testing, as defined by appropriate animal selection and sample collection, can potentially provide direct confirmatory evaluation of metabolic health and disease risk or evaluate nutritive status on a herd basis.

A "metabolic profile" is defined as a series of specific analytical tests run in combination and used as a herd-based rather than individual-based, diagnostic aid. Use of metabolic profile is the result of technologic improvements in analytical instrumentation, which can completel multiple analyses in a short time period.

Bibliography

Historical Perspectives of Metabolic Profiling

  • The Compton Metabolic Profile Test. J. M. Payne. Proc R Soc Med. 1972 Feb;65(2):181-3
  • The use of a metabolic profile test in dairy herds. J. M. Payne, S. M. Dew, R. Manston, M. Faulks. Vet Rec. 1970 Aug 8;87(6):150-8
  • Blood Metabolic Profiles: Their Use and Relation to Nutritional Status of Dairy Cows. A. J. Lee, A. R. Twardock, et al. 1978. J. Dairy. Sci. 61(11):1652-1670

Reviews of Metabolic Profiling

  • Blood Metabolic Profiles: Their Use and Relation to Nutritional Status of Dairy Cows. Lee, A. J., A. R. Twardock, et al. (1978). J. Dairy Sci. 61(11):1652-1670
  • Week-to-week variation in blood composition of dairy cows and its effect on interpretations of metabolic profile tests. Rowlands, G. J. 1984. Br Vet J 140(6): 550-7.
  • Metabolic Profile Testing in Dairy Herds: Wrong Answer or Wrong Question? Eicher, R. Acta Veterinaria Scandinavica 2003, 44(Suppl 1) p28.

Applications of Metabolic Profiling as a Herd Screening Tool

  • Use and Limitations of profiles in assessing health or nutritional status of dairy herds. R. S. Adams, W. L. Stout, et al. 1978. J. Dairy Sci. 61(11):1671-1679.
  • Relationships of metabolic profiles to milk production and feeding in dairy cows. Kida, K. 2003. J Vet Med Sci 65(6): 671-7.
  • Use of metabolic profiles for the assessment of dietary adequacy in UK dairy herds. Macrae, A. I., D. A. Whitaker, et al. 2006. Vet Rec 159(20): 655-61.
  • Use of Every Ten-Day Criteria for Metabolic Profile Test after Calving and Dry Off in Dairy Herds. K. Katsuya. J. Vet. Med. Sci. 2002 64(11):1003-1010.
  • Metabolic Profiles in Virginia Dairy Herds of Different Milk Yields. Jones, G. M., E. E. Wildman, et al. (1982). J. Dairy Sci. 65(4): 683-688.
  • Studies of the transition cow under a pasture-based milk production system: metabolic profiles. Cavestany, D., J. E. Blanc, et al. 2005 J Vet Med A Physiol Pathol Clin Med 52(1): 1-7.
  • Metabolic profiles and progetsterone cycles in first lactation dairy cows. Taylor, V. J., D. E. Beever, et al. 2003. Theriogenology 59(7): 1661-77.
  • Metabolic profile testing for Jersey cows in Louisiana: reference values. Roussel, J. D., S. H. Seybt. et al. 1982. Am J Vet Res 43(6): 1075-7.
  • First lactation ovarian function in dairy heifers in relation to prepubertal metabolic profiles. Taylor, V. J., D. E. Beever, et al. 2004. J Endocrinol 180(1): 63-75.
  • Use of metabolic profiles in dairy cattle in tropical and subtropical countries on smallholder dairy farms. Whitaker, D. A., W. J. Goodger, et al. 1999. Prev Vet Med 38(2-3): 119-31.

Metabolic Profiling in Assessing Disease Conditions

  • Metabolic parameters and blood leukocyte profiles in cows from herds with high or low mastitis incidence. Holtenius, K., K. Persson Waller, et al. 2004. Vet J 168(1): 65-73.
  • The metabolic profile test: its practicability in assessing feeding management and periparturient diseases in high yielding commercial dairy herds. Kida, K. 2002 J Vet Med Sci 64(7): 557-63.
  • Metabolic profile in cows in the peripartum period with and without retained placenta. Kudlac, E., M. Sakour, et al. 1995. Vet Med (Praha) 40(7): 201-7.
  • Metabolic profiles around calving in five high-producing Swedish dairy herds with a history or abomasal displacement and ketosis. Stengarde, L., M. Traven, et al. 2008. Acta Vet Scand 50(1): 31.
  • Reducing Dry Period Length to Simplify Feeding Transition Cows: Milk Production, Energy Balance, and Metabolic Profiles. Rastani, R. R., R. R. Grummer, et al. (2005). "." J. Dairy Sci. 88(3): 1004-1014.
  • Effects of Supplemental Vitamin E on the Performance and Metabolic Profiles of Dairy Calves. Reddy, P. G., J. L. Morrill, et al. (1985). J. Dairy Sci. 68(9): 2259-2266.
  • Macromineral disorders of the transition cow. Goff, J. P. (2004). Vet Clin North Am Food Anim Pract. 20(3): 471-94, v.

More Metabolic Profiling Resources

Methodologies used in metabolic profiling have ranged from mean analysis of multiple analytes to proportional analysis of single analytes. Perparturient disease is a result of the cow's inability to maintain coordinated metabolism between lipid, glucose and amino acids. Use of pooled samples was evaluated as a method to collect usable information on herd metabolic status encompassing multiple parameters without the high cost of individual sampling. Aim of this study was to determine if diagnostic interpretation guidelines can be established for pooled metabolic profile sample

The original intent of the CMP was to monitor metabolic health of the heard, help diagnose metabolic problems and production diseases, and identify metabolically superior cows. Interpretation issues and a lack of specificity in differentiating normal from problem herds coupled with high inherent costs with little diagnostic returns has limited the application of the CMP test, especially within the United States. Research since the time of CMP development has clarified many metabolic issues of the transition cow and its relationship to the periparturient disease. In concert with this improved understanding of integrated transition metabolism there has been improvement in technical methods to assess metabolic status. Additionally, the shift to increasing herd size and recognition of significant health, production, and economic consequences of periparturient disease has led to renewed interest in a revised metabolic profile application in monitoring transition cow health and disease risk.

Early Publications:

  • The Compton Metabolic Profile Test. J. M. Payne. Proc R Soc Med. 1972 Feb;65(2):181-3
  • The use of a metabolic profile test in dairy herds. J. M. Payne, S. M. Dew, R. Manston, M. Faulks. Vet Rec. 1970 Aug 8;87(6):150-8
  • Blood Metabolic Profiles: Their Use and Relation to Nutritional Status of Dairy Cows. A. J. Lee, A. R. Twardock, et al. 1978. J. Dairy. Sci. 61(11):1652-1670
  • Use and Limitations of profiles in assessing health or nutritional status of dairy herds. R. S. Adams, W. L. Stout, et al. 1978. J. Dairy Sci. 61(11):1671-1679.

Why would I want to use metabolic profiling on my farm?

  • Assess nutritional status of feeding group of herd. Ration evaluation is the cornerstone of herd nutritional assessment, but can be fraught with uncertainty and difficulty in obtaining true measure of dry matter or nutrient intake. Metabolic profiling, using specific parameters known to be responsive to dietary intake, can be used to complement dietary evaluation of current feeding program adequacy or a response to a feeding program change.
  • Identify disease conditions early. Metabolic profiling using defined analytes (beta-hydroxybutyrate [BHB], calcium [Ca], magnesium [Mg], rumen pH) can be used to assess prevalence of various subclinical metabolic diseases (ketosis, hypocalcemia, hypomagnesemia, subacute rumimnal acidosis [SARA], respectively) in the absence of obvious clinical disease problems.
  • Identify potential risk for disease problems. Specific blood analytes that are either high or low relative to defined reference or cut point values prior to calving or immediately postpartum can predict potential for increased risk of experiencing specific or collective periparturient disease events.
  • Survey for potential causes of disease problems. Broad-based metabolic profiling can be used as a screening tool to direct the focus of a herd investigation. Profile results need to be interpreted relative to diet-specific problems as well as management and other factos that may secondarily alter animal response to diet.

Define the problem to be addressed.

Define a given herd situation by asking a specific question. Are the heifers experiencing subclinical ketosis? Why are mature cows experiencing more retained placenta? Why is the herd experiencing more periparturient disease? Consider pertinent comparisons of interest relative to the defined problem and identify which cow populations are of concern to be sampled.

Once the herd problem has been defined a grouping strategy for sample collection can be constructed (Table 1). In addressing transition cow problems, blood analyte concentrations from cows just prior and immediately following calving are the most diagnostic. As a result of tremendous individual variation, cows should not be sampled within 3 days prior to or following calving. Others suggest samples immediately pre- and postpartum be avoided citing large analyte variability and recommend sampling fresh cows at 25-80 days in milk. Although blood analyte concentrations from far off dry cows (> 30 days prior to calving) are not predictive for postpartum disease risk, results can be used as a reference point for comparison to other groups, or values may be diagnostic within themselves for some disease entity. The group or groups of cows selected for analysis will depend upon the problem definition and desired sampling approach.

Table 1. Suggested grouping strategies for collecting blood samples in completing metabolic profile testing using individual or pooled samples.
Physiologic GroupsTime Relative to CalvingParityDisease Status
Far Off Dry>10 days following dry off and > 30 prior to expected calvingWithin any group keep heifers and 2+ lactation animals separate - pool as separate parity groups within physiologic groupsUnknown
Close-up DryBetween 3 and 30 days prior to calving (7 to 21 days preferred)Within any group keep heifers and 2+ lactation animals separate - pool as separate parity groups within physiologic groupsUnknown
Fresh3 to 30 days in milk (7 to 21 days preferred)Within any group keep heifers and 2+ lactation animals separate - pool as separate parity groups within physiologic groupsGroup cows with and without disease within lactation groups - keep days milk in similar within and between groups
Lactation GroupsDefine as needed based on disease conditions, production level or other problemWithin any group keep heifers and 2+ lactation animals separate - pool as separate parity groups within physiologic groupsGroup cows with and without disease within lactation groups - keep days milk in similar within and between groups

Cows to be selected within the defined groups for a metabolic profile should be free of obvious clinical disease. By selecting cows defined as "clinically normal", outlier analyte concentrations associated with disease are removed, thus better highlighting potential differences resulting from nutritional or subclinical disease problems. One may elect to sample cows affected with specific diseases for comparison to cows of similar days in milk that are not affected. Differences in blood analyte concentrations between clinically affected and unaffected cows may provide some direction as to underlying problems associated with disease pathogenesis.

Define the testing procedure approach.

Metabolic profiling utilizes the same clinical chemistry tests performed in disease diagnosis. However, testing methods are herd-based for metabolic profiling rather than individual-based for disease diagnosis. Herd-based testing can be categorized into two approaches, targeted diagnostics and screening tool.

The screening tool approach is consistent with traditional metabolic profiling methods where multiple analytes are determined within selected group or groups of cows. Determination of multiple analytes is predicated on the concept that periparturient metabolic disease is a result of the cow's inability to maintain coordinated interrelationships between lipid, glucose and amino acid metabolism. A screening tool approach to metabolic profiling can be used as a broad-based diagnostic evaluation of herd nutritive status, assessment of disease risk factors, or indicator of potential factors responsible for disease conditions. Limitations to the screening tool approach are high testing costs and potential interpretation issues. A pooled-sample process has been advocated to address cost concerns and maintain a wide analyte array in assessing herd nutritional or disease risk status. Predictive disease risk relationships have been well established with specific analytes, though multiple analyte indices or analyte combinations may provide a better indication of metabolic stability or instability. Unfortunately, few data are available to provide sound reference values for interpretation.

The targeted diagnostic approach utilizes well defined diagnostic analytes to determine herd risk for specific "gateway" periparturient diseases. Elevated prepartum NEFA concentration and postpartum BHB concentration are recognized risk factors for ketosis and left displaced abomasum. Low blood calcium concentration immediately postcalving is a risk indicator for subclinical hypocalcemia. Blood UN is a potential indicator for assessing herd protein status. In this approach, specific analyte concentration is determined and compared to specific threshold criteria. Percent of individuals above (NEFA and BHB) or below (calcium) is used to interpret herd disease risk. Urea nitrogen values are interpreted as a mean value for the individuals within a defined group. Individual testing, lower testing costs, and ease of interpretation are strengths of this approach. Limitation of this approach is scope of analytes determined.

Which approach to be used in evaluating a herd will depend upon the problem to be addressed, herd size, and cost limitations. Smaller dairy herds (< 120 cows) will not have a large enough population of animals to be sampled within defined physiologic groups for the screening tool approach compared to large herds. With limited animal numbers, individual testing or collecting samples over time are possible approaches. Costs are the single most limiting factor to metabolic profiling. Multiple analyte testing services range in cost from $17 to $50 per sample depending upon the number of blood analytes measured and laboratory pricing structure. This makes individual testing in multiple groups nearly cost prohibitive, thus the rationale for pooled samples. Using the single analyte approach, cost may range from $3 to $10 per sample depending upon specific analyte of interest and laboratory pricing structure.

Determine sampling process and number

Few would argue the strength of individual analysis in metabolic profile analysis. Indeed the gold standard for analytical analysis would be to measure a large percent of the population of interest as individuals. Decisions made in grouping and approach strategies will define the suggested number of individuals within a group to be sampled.

Individual Sampling

Statistical modeling would suggest at least 8 individuals from a population is representative (mean analysis), though 12 or 13 samples are best for threshold analysis using the targeted diagnostic approach. Though statistics may show a minimum number of samples needed to represent a population, the best way to truly characterize a population is to obtain more samples. The goal in metabolic profile testing is to appropriately balance quality of information derived from the testing process with analytical costs.

Pooled Sampling

In the original protocol for the CMP, mean analyte values within physiologic groupings were used for interpretation. These mean values were arithmetically determined from individual samples. Use of individual sampling resulted in the high associated costs of this procedure. In place of individual analysis, can pooled samples be used to reduce the cost and provide some valid method of herd assessment? In a number of preliminary studies using individual samples, pooled samples from these individuals were found to accurately represent arithmetic means from pool sizes ranging from 5 to 20 animals. If sufficient animals are available within a large group, consider taking multiple pooled samples to account for a greater number of animals and provide some degree of variation assessment within the group.

Specific analytes determined for any herd metabolic profile will be dependent upon the metabolic profile sampling strategy described previously. The original CMP test measured 13 different analytes that included packed cell volume, hemoglobin, glucose, blood urea nitrogen (BUN), total protein, albumin, calcium (Ca), inorganic phosphorus (Pi), magnesium (Mg), potassium (K), sodium (Na), copper (Cu), and iron (Fe).

Energy Balance

Energy balance is by and far one of the most critical nutritional factors impacting on animal health, lactation, and reproductive performance. Traditionally, we have monitored changes in energy balance via body weight and condition score changes over time. This procedure may not be a sensitive enough tool when dealing with the transition cow.

Nonesterified fatty acids (NEFA) have become the mainstay in determination of energy balance. Many research studies have shown good correlations between energy balance and serum NEFA concentrations. Concentration of NEFA directly reflects the amount of adipose (fat) tissue breakdown taking place. Excessively high NEFA concentrations due to negative energy balance either prepartum or early postpartum are predictive for increased risk of ketosis, left displaced abomasums, and most other periparturient diseases.

ß-hydroxybutyrate, one of the ketone bodies, is another parameter useful in assessing energy status. However, BHB can come from diestary sources (poorly fermented silage) and not reflect aberrant metabolism. Prior to calving, BHB concentrations are not predictive for disease risk but may be elevated if the animal is in negative energy balance or consuming ketogenic silage. Following calving, BHB concentrations are diagnostic for disease and predictive for periparturient disease problems.

Blood glucose concentration, as an independent test, is not a good indicator of energy status as a result of tight homeostatic control. However, glucose concentrations measured in conjunction with other tests may provide some further insight into underlying mechanisms of disease (Type I vs. Type II ketosis).

Protein Evaluation

At present there is no single metabolite that can be measured that directly reflects protein status. As a result, multiple parameters are needed to assess protein status including UN, creatinine, total protein, albumin and creatine kinase (Ck). Urea nitrogen concentrations are influenced by a wide variety of interrelated parameters including: dietary protein intake and rumen degradability, dietary amino acid composition, protein intake relative to requirement, liver and kidney function, muscle tissue breakdown, and dietary carbohydrate amount and rumen degradability. Creatinine is used to assess renal function and its impact on UN values. Creatin kinase is released from muscle when it is catabolized to supply needed amino acids or when injured.

Total protein and albumin reflect availability of amino acids and their concentration decline in the face of protein deficiency. However, this occures over a period of time. Albumin has a relatively short half-life and can reflect protein deficiency problems over a period of a month or two. Albumin was found to be associated with postpartum disease and can be used to predict disease risk in close-up and fresh periods. Other nutrients, namely iron and vitamin A, might also reflect protein status as both require a carrier protein synthesized in the liver. Lower concentrations of either nutrient may be observed when amino acid availability is limited, liver function is compromised, or both.

Liver Function

Liver function can be assessed through a variety of enzymes: gamma-glutamyltransferase (GGT), aspartate aminotransferase (AST), and sorbitol dehydrogenase (SDH) and total bilirubin concentrations in the blood. Unfortunately, an elevation in any of these parameters does not suggest anything more than some insult has occurred to the liver. Muscle catabolism or injury can result in elevated blood AST activities. Bilirubin values are most specific to bile flow problems that overt liver cell damage.

Since these parameters are not specific to liver function, other liver function indices have been advocated. A liver activity index parameter that accounts for changes in albumin, cholesterol and total bilirubin over the first 28 days follwing calving has been defined. Although a robust diagnostic tool, its use requires multiple samples from the same cow over a period of time. This would preclude its use within a typical metabolic profiling approach.

Calculating the NEFA to cholesterol ratio (molar bases) to assess the liver's ability to export incoming NEFA has been advocated. Calculated NEFA to cholesterol ratio was predictive for postpartum disease in the close-up dry and fresh cows.

Macromineral Evaluation

Macrominerals Ca, Pi, K, Mg, Na, chloride (Cl), and sulfer (S) are of extreme interest as to their status relative to their role in milk fever, alert downer cows, and weak cow syndrome. Unfortunately, most of these minerals are tightly regulated in the body through a variety of homeostatic processes. Blood concentrations of macrominerals are not reflective of dietary status when the homeostatic system is functioning property. Phosphorus, K, Mg, and S are macrominerals in which blood concentrations are somewhat sensitive to dietary intake. Electrolytes Na,Cl, and K are altered when renal or digestive function is compromised or in extreme dietary deficiency states.

Assessment of Ca concentrations around the time of calving is a useful indicator of how well the Ca regulatory system is working and potential for clinical or subclinical hypocalcemia problems. Despite concerns about homeostatic regulation, pre- and postpartum concentrations of Ca, Mg, Na, and K were found to be predictive of specific postpartum disease risk. Surprisingly, blood P concentrations were not found to be predictive of disease risk, but abnormal values still may provide some diagnostic significance.

Other Possible Analytes

Research into the role of inflammatory mediators as a contributing factor to periparturient disease pathogenesis has led to interest in measuring markers of an activated inflammatory response as part of metabolic profiling. Specific acute phase proteins ceruloplasmin and haptoglobin have been routinely determined by some investigators. Analytical tests for these acute phase proteins are not readily available outside of research laboratories at the current time. Other acute measures of inflammatory mediator activation (i.e., heat shock proteins) and oxidative stress markers may provide further insight, but are not currently available for use in metabolic profiling.


Figure 1. Collecting blood from the tail (coccygeal) vein.

  • Blood samples should be taken from either jugular or coccygeal veins with a minimal amount of stress. Lower concentrations of Pi and K have been documented in jugular compared to coccygeal blood samples as a result of salivary gland uptake. Blood samples from the mammary veins are not appropriate given the loss of nutrients into the mammary gland.
  • Vacuum tubes are color coded (Figure 2) for specific diagnostic test procedures based on the specific anticoagulant or additive present in the tube (Table 1). Plasma from green top tubes is generally preferred, but red top (serum) tubes can be used. It is best to ask the laboratory which sample is preferred.
    Table 1 - Description of blood collection tubes used for metabolic profiles
    Stopper ColorAdditiveSample ObtainedIntended Use/Disadvantages

    Red

    NoneSerumRoutine use for all tests. Prolonged clot exposure results in decreased glucose and Ca and increased phosphorus. Hemolysis problems in poorly handled sample (Figure 3)
    GrayNa Fluoride or K OxalateSerumGlycolytic inhibitor for sensitive glucose analysis
    Royal BluePlastic Stopper
    Na Heparin
    Serum, Plasma, or Whole BloodTrace mineral analysis, especially Zn
    LavenderEDTAWhole Blood, PlasmaRoutine use for Complete Blood Count/ EDTA chelates Ca, Mg and decrease enzyme activities
    GreenNa HeparinPlasma, Whole BloodRoutine analyses for either plasma or whole blood/ No effect on metabolites
    Red and GraySerum Separator plugSerumDuring centrifugation gel plug moves to completely separate the serum from the clot/ hemolysis can be a problem
  • All Samples should be properly identified with animal and group identification and date of collection. Use herd records to ensure the selected animals fit the defined group parameters, especially relative to parity and days in milk (or time relative to calving). Other pertinent information for interpretation of the metabolic profile would include milk production level, milk composition, pregnancy status, and body condition score. Again, metabolic profiling should only be used as a complement to more traditional diagnostic procedures.
  • Recognize time of sampling relative to feeding and feeding management may also influence metabolite concentrations. If non-esterified fatty acid (NEFA) concentrations are of specific interest, then samples are best collected prior to the first primary feeding bout. If beta-hydroxy-butyrate (BHB) or blood urea nitrogen (BUN) is of primary interest, then samples are best collected when convenient and account for feeding time effects. This will be less of a concern in TMR-fed herds. If herds are being repeatedly sampled as a monitoring tool, samples should be taken at approximately the same time of day to minimize the diurnal and prandial variation between sampling periods.
  • Meticulous effort should be taken to prevent hemolysis of any sample. All samples should be iced, but not frozen, immediately after collection and kept refrigerated until processed.
  • Samples should be transported on ice (Figure 4) until properly refrigerator and shipped to the laboratory.


Figure 2. Color coded sample collection tubes commonly used for metabolic profiling.


Figure 3. Various stages of hemolysis in serum samples. Deeper shades of red indicate more severe hemolysis than lighter shades.


Figure 4. Samples properly stored in cooler on ice for transport after collection.


Figure 1. Properly balanced samples loaded into centrifuge.

For serum samples, the clot should be removed as quickly as possible (within hours of collection). Although serum separator tubes are convenient, experience suggests samples are prone to have some degree of hemolysis (see "Sample Collection", Figure 2) and prolonged clot contact. Simple red top tubes are the best choice for serum collection.

Samples should be centrifuged at 2200 rpm for 20 minutes to remove the clot. Serum can then be drawn off the top and transferred to a new red top tube for submission to the lab.

Preparing Individual Samples:

  • For individual samples, pipet the total amount of serum needed by the lab from the collection tube into a new red top tube.

Preparing Pooled Samples:

  • In preparing pooled samples one must be meticulous in precisely measuring equal amounts of serum from each individual to be included in the pooled sample. Depending upon the total number to be included, typically between 100 and 500 microliters (0.1 to 0.5 ml) from each individual are mixed into a new clean test tube (7 to 10 ml capacity). This process is best completed with a micropipettor or a TB syringe for precision.
  • To determine the amount needed from each sample, take the total amount of serum needed by the lab and divide by the total number of samples in the pool.

Example: The lab needs 2.5 ml or 2500 microliters of serum. There are 8 animals in the pool.

2500/8 = 310 microliters (0.31 ml) of each sample to be combined


Figure 2. Calculating the amount needed from each individual sample in pool.


Figure 3. Pipetting the precise amount of individual sample from the collection tube into the new pooled test tube.


Figure 4. Finished pooled sample. An equal amount of each individual sample has been pooled together for a combined total of the amount needed by the lab.

  • Pooled samples should be adequately mixed then directly submitted to the laboratory or frozen and shipped. Allowing pooled samples to remain at room temperature for a period of time (>1 hr) potentially will result in gel formation that will adversely affect laboratory analysis.

Shipping Samples:

Once the serum or plasma has been harvested from the original sample, it should be frozen and shipped to the laboratory. Alternatively harvested samples can remain refrigerated and shipped on ice to the laboratory. Most laboratories recommend overnight shipping to minimize any sample deterioration. Contact the laboratory before shipping samples as many laboratories may have specific days of the week that samples can be received for metabolic profiling.


Figure 5. Blood samples packed in an insulated shipping box with icepacks. The samples are in a cardboard blood-box, inside of a plastic bag.

Testing Laboratories:

Your samples should be submitted to an appropriate laboratory such as:

Oregon State University College of Veterinary Medicine

Michigan State University

Cornell University College of Veterinary Medicine

Texas A & M University

Interpretation of Metabolic Profiling Results

Characterizing interpretation parameters to obtain valid information is the true challenge to metabolic profile testing.

The Need for Reference Ranges

A functional understanding of underlying metabolic and physiologic mechanisms controlling blood metabolite concentrations is necessary to properly interpret metabolic profiles and their application. One must appreciate the fundamental philosophic difference in blood analyte concentration interpretation between disease diagnosis and metabolic profiling paradigms. Disease diagnosis is focused on identifying critical outliers when compared to the population as a whole (i.e., 95% reference range). By definition, individuals sampled for metabolic profiling are expected to be within the 95% reference range for the population as they are "clinically normal". However, "normal" animals can be at risk for experiencing a disease if their metabolic status is trending away from the population central tendency. Blood analyte concentrations measure a continuous spectrum between health and disease and cannot be simply interpreted as "black or white". Metabolic profile criteria are more restrictive than the whole population and interpretation is based on statistical associations to disease risk. The single most important aspect of metabolic profiling is establishing valid reference values for comparison. (See Reference Values)

Caution! Same results, different causes?

It is very important for metabolic profile test results to be interpreted in light of animal, dietary, and environmental assessments. This point cannot be over-emphasized, metabolic profile testing results do not always indicate nutrition or diet to be the underlying problem. For example, inadequate dietary energy density is not the only reason for elevated NEFA concentrations. Inadequate dry matter intake as a results of heat stress, overcrowding, poor forage quality, competitive social interactions, inadequate feed availability or some combination of these could also account for observed negative energy balance. Results of metabolic profiling, depending upon the approach chosen, can provide disease diagnosis or insights into how to direct one's diagnostic investigation. The power of metabolic profiling comes from an ability to integrate multiple parameters to determine scope of metabolic aberrations present. Certain combinations of analyte alterations can provide insight into underlying disease pathogenesis and severity. Optimistically, protocols for disease mitigation and prevention can then be designed appropriately with some increased potential for success.

Blood tests from individual animals are routinely used to diagnose disease problems in dairy cattle. Veterinarians, producers and nutrition consultants alike seem to be interested in extracting pertinent information relative to herd nutrition and health status from blood tests. The Compton Metabolic Profile (CMP) has traditionally been used in this approach (10). The original intent of the CMP was to:

  1. monitor metabolic health of the herd;
  2. help diagnose metabolic problems and production diseases, and
  3. identify metabolically superior cows (10, 11). A "metabolic profile" is defined as a series of specific analytical tests run in combination and used as a diagnostic aid (7).

Interpreting Individual Samples

There are two possible methods used when interpreting individual samples; targeted diagnostics and the screening tool.

Targeted Diagnostics

Using the targeted diagnostic approach, 8 (mean-based analytes) or 12 (threshold-based analytes) individual samples were obtained from a select group of animals at risk for a disease of interest. Individual analyte results are compared to the reference value and individual values exceeding (BEFA or BHB) or below (Ca) the defined threshold value are identified as abnormal. Proportion of abnormal results is calculated and compared to defined criteria. Criteria are defined by statistical calculations accounting for desired confidence the value represents the group sampled and disease prevalence rate (alarm level) that warrants intervention. Clinical decisions require less confidence intervals and 70 to 75% is suggested compared to the 95% confidence interval typically require for research. For disease alarm levels of 10 and 25%, abnormal results should exceed 2 of 12 or 4 of 12, respectively. As the proportion of positive (abnormal values) increases, disease prevalence would be expected to increase. With a positive herd diagnosis, further diagnostics to determine a potential cause can be initiated as well as disease-specific treatment protocols can be enacted.

Screening Tool

Using the screening tool approach, a predetermined array of analytes is determined on individual samples collected from a select group or groups of cows. Analyte results from individual cows are compared to reference values for the defined group and number of abnormal results determined. As the proportion of abnormal values increases, disease risk increases. Response will depend up on the specific analyte or analytes found to be abnormal. Specific patterns of analyte changes, similar to disease diagnosis, would suggest an associated disease and defined treatment and prevention protocols can be enacted. If only NEFA concentrations were elevated, then one would be most directed to causes of insufficient energy intake. If in addition AST activities were elevated and cholesterol concentrations reduced, then liver dysfunction (fatty infiltration) would be suspected. Reduced UN and albumin concentrations of BHB and glucose, respectively, would provide insight into status of glucose homeostasis and potential for Type I or II ketosis being present.

Interpreting Pooled Samples

If one is using a pooled sample collection process, more than likely samples will be collected from multiple groups of cows (early dry, close-up dry, fresh).

Within the original CMP test, individual analyte values within the three defined groups were averaged and a group mean was compared to herd-based reference values. In comparison to individual samples, mean values lose the measure of individual variability. Mean values need to be compared to appropriate reference values and these will differ from those used for individual samples. Herd mean reference values should be generated using means of healthy cows within a herd and averaged across herds rather than just an average of all individuals. Interpretation of mean values is different from individual samples where you are comparing to the variation of the reference population. Mean values must be compared to variation of the reference population central tendency (median or mean). Criteria for interpreting mean values were not well defined and this was an interpretation problem with the CMP test. It was recognized that mean values could not span within the 95% confidence range for the reference population. Early adopters of metabolic profiling empirically suggested mean values should range within 1 or 1.3 standard deviations in the reference population.

Recent studies further investigated mean sample value interpretation using individual or pooled sampling procedures. Across all analytes measured, mean values of individuals or pooled samples that had less than 10% abnormal values deviated 0.26 standard deviations from the reference population mean (or median). However, the amount of deviation was found to be analyte specific and ranged from 0.11 (glucose) to 0.6 (BHB) standard deviations. A linear association was found between the number of standard deviations the measured mean or pooled sample moved away from the reference population mean (or median) and percent of abnormal values within the group sample. Using these relationships, analyte concentrations guidelines for interpretation of mean or pooled samples can be generated (Table 1). At present, interpretation guidelines are available for fresh cow samples. Similar diagnostic interpretations as previously described can be accomplished with the various analytes measured in mean or pooled samples.

If one is using a pooled sample collection process, more than likely samples will be collected from multiple groups of cows (early dry, close-up dry, fresh). Beyond the described analyte concentration evaluation within a cow grouping, comparisons can be made across groups. Patterns in specific blood analyte concentrations across transition cow groups might provide some insightful clues to underlying problems and potential solutions. Measuring analytes across groups might also provide some time dimension as to source and length of a problem. Herds experiencing low protein status in fresh cows typically have low UN and normal albumin concentrations in early dry cows and these values decline over the close-up dry and fresh periods, indicative of inadequate dietary protein intake.

Another method to diagnostically assess analyte changes over time is to use a variation on statistical process control. Mean group or pooled sample analyte concentrations repeatedly measured over a time frame can be plotted over time. Overall mean and statistical variation within and between time periods can be determined to interpret potential changes in animal performance. Individual test results outside of control limits or 3 or more consecutive means above or below overall average would indicate significant changes in analyte concentrations. Trends in mean value can then be related back to changes in diet, management, or environment to determine source of problems or document improvement following changes.

Table 1. Proposed guidelines for fresh cow mean or pooled sample interpretation. Parenthesis indicate mean analyte value (70% confidence interval).
AnalyteUnits0% Abnormal Values in Pool20% Abnormal Values in Pool40% Abnormal Values in Pool
Albuming/dL3.85
(3.77-3.92)
3.68
(3.57-3.70)
3.51
(3.38-3.63)
ASTIU/L93.8
(88.8-98.7)
99.3
(91.8-106.7)
104.7
(94.9-114.6
BHBmg/dL5.21
(4.3-6.2
9.03
(7.6-10.4)
12.84
(11.0-14.7)
Calciummg/dL9.67
(9.59-9.79)
8.68
(8.37-8.99)
7.68
(7.16-8.20)
Glucosemg/dL60.2
(58.6-61.9)
56.9
(54.7-59.1)
>53.7
(50.9-56.4)
Magnesiummg/dL2.54
(2.49-2.60)
2.33
(2.23-2.42)
2.11
(1.97-2.24)
NEFAmEq/L0.299
(.263-.336)
0.454
(.397-.512)
0.609
(.531-.687)
NEFA:Chol. Ratiommol/L:mmol/L0.116
(.098-.134)
0.215
(.181-.250)
0.315
(.264-.365)
Urea Nmg/dL17.83
(16.9-18.8)
15.87
(14.5-17.2)
13.91
(12.2-15.6)

Reference:

Anderson, D. E. and Rings M. (2009) Current Veterinary Therapy: Food Animal Practice St. Louis, MO: Saunders Elsevier.

Resources

Blood tests from individual animals are routinely used to diagnose disease problems in dairy cattle. Veterinarians, producers and nutrition consultants alike seem to be interested in extracting pertinent information relative to herd nutrition and health status from blood tests. The Compton Metabolic Profile (CMP) has traditionally been used in this approach (10). The original intent of the CMP was to:

  1. monitor metabolic health of the herd;
  2. help diagnose metabolic problems and production diseases, and
  3. identify metabolically superior cows (10, 11). A "metabolic profile" is defined as a series of specific analytical tests run in combination and used as a diagnostic aid (7).
  • Use of metabolic profiles for the assessment of dietary adequacy in UK dairy herds. Macrae, A. I., D. A. Whitaker, et al. 2006. Vet Rec 159(20): 655-61.
  • The metabolic profile test: its practicability in assessing feeding management and periparturient diseases in high yielding commercial dairy herds. Kida, K. 2002 J Vet Med Sci 64(7): 557-63.
  • Metabolic profile in cows in the peripartum period with and without retained placenta. Kudlac, E., M. Sakour, et al. 1995. Vet Med (Praha) 40(7): 201-7.
  • Interpretation of pooled metabolic profiles for herd assessment. Davidek, J., Van Saun, R.J. Pp. 24, In: Oral and Poster Abstracts 25th Jubilee 25th World Buiatrics Congress, Budapest, Hungary, July 6-11, 2008.
  • Interpretation of Pooled Metabolic Profiles for Evaluating Transition Cow Health Status (Poster). Robert J. Van Saun, DVM, MS, PhD, Diplomate ACT and ACVN. Department of Veterinary Sciences, Pennsylvania State University

Targeted Diagnostics

Using the targeted diagnostic approach, 8 (mean-based analytes) or 12 (threshold-based analytes) individual samples were obtained from a select group of animals at risk for a disease of interest. Individual analyte results are compared to the reference value and individual values exceeding (BEFA or BHB) or below (Ca) the defined threshold value are identified as abnormal. Proportion of abnormal results is calculated and compared to defined criteria. Criteria are defined by statistical calculations accounting for desired confidence the value represents the group sampled and disease prevalence rate (alarm level) that warrants intervention. Clinical decisions require less confidence intervals and 70 to 75% is suggested compared to the 95% confidence interval typically require for research. For disease alarm levels of 10 and 25%, abnormal results should exceed 2 of 12 or 4 of 12, respectively. As the proportion of positive (abnormal values) increases, disease prevalence would be expected to increase. With a positive herd diagnosis, further diagnostics to determine a potential cause can be initiated as well as disease-specific treatment protocols can be enacted.

Screening Tool

Using the screening tool approach, a predetermined array of analytes is determined on individual samples collected from a select group or groups of cows. Analyte results from individual cows are compared to reference values for the defined group and number of abnormal results determined. As the proportion of abnormal values increases, disease risk increases. Response will depend up on the specific analyte or analytes found to be abnormal. Specific patterns of analyte changes, similar to disease diagnosis, would suggest an associated disease and defined treatment and prevention protocols can be enacted. If only NEFA concentrations were elevated, then one would be most directed to causes of insufficient energy intake. If in addition AST activities were elevated and cholesterol concentrations reduced, then liver dysfunction (fatty infiltration) would be suspected. Reduced UN and albumin concentrations of BHB and glucose, respectively, would provide insight into status of glucose homeostasis and potential for Type I or II ketosis being present.

Reference:

Anderson, D. E. and Rings M. (2009) Current Veterinary Therapy: Food Animal Practice St. Louis, MO: Saunders Elsevier.

Within the original CMP test, individual analyte values within the three defined groups were averaged and a group mean was compared to herd-based reference values. In comparison to individual samples, mean values lose the measure of individual variability. Mean values need to be compared to appropriate reference values and these will differ from those used for individual samples. Herd mean reference values should be generated using means of healthy cows within a herd and averaged across herds rather than just an average of all individuals. Interpretation of mean values is different from individual samples where you are comparing to the variation of the reference population. Mean values must be compared to variation of the reference population central tendency (median or mean). Criteria for interpreting mean values were not well defined and this was an interpretation problem with the CMP test. It was recognized that mean values could not span within the 95% confidence range for the reference population. Early adopters of metabolic profiling empirically suggested mean values should range within 1 or 1.3 standard deviations in the reference population.

Recent studies further investigated mean sample value interpretation using individual or pooled sampling procedures. Across all analytes measured, mean values of individuals or pooled samples that had less than 10% abnormal values deviated 0.26 standard deviations from the reference population mean (or median). However, the amount of deviation was found to be analyte specific and ranged from 0.11 (glucose) to 0.6 (BHB) standard deviations. A linear association was found between the number of standard deviations the measured mean or pooled sample moved away from the reference population mean (or median) and percent of abnormal values within the group sample. Using these relationships, analyte concentrations guidelines for interpretation of mean or pooled samples can be generated (Table 1). At present, interpretation guidelines are available for fresh cow samples. Similar diagnostic interpretations as previously described can be accomplished with the various analytes measured in mean or pooled samples.

If one is using a pooled sample collection process, more than likely samples will be collected from multiple groups of cows (early dry, close-up dry, fresh). Beyond the described analyte concentration evaluation within a cow grouping, comparisons can be made across groups. Patterns in specific blood analyte concentrations across transition cow groups might provide some insightful clues to underlying problems and potential solutions. Measuring analytes across groups might also provide some time dimension as to source and length of a problem. Herds experiencing low protein status in fresh cows typically have low UN and normal albumin concentrations in early dry cows and these values decline over the close-up dry and fresh periods, indicative of inadequate dietary protein intake.

Another method to diagnostically assess analyte changes over time is to use a variation on statistical process control. Mean group or pooled sample analyte concentrations repeatedly measured over a time frame can be plotted over time. Overall mean and statistical variation within and between time periods can be determined to interpret potential changes in animal performance. Individual test results outside of control limits or 3 or more consecutive means above or below overall average would indicate significant changes in analyte concentrations. Trends in mean value can then be related back to changes in diet, management, or environment to determine source of problems or document improvement following changes.

Table 1. Proposed guidelines for fresh cow mean or pooled sample interpretation. Parenthesis indicate mean analyte value (70% confidence interval).
AnalyteUnits0% Abnormal Values in Pool20% Abnormal Values in Pool40% Abnormal Values in Pool
Albuming/dL3.85
(3.77-3.92)
3.68
(3.57-3.70)
3.51
(3.38-3.63)
ASTIU/L93.8
(88.8-98.7)
99.3
(91.8-106.7)
104.7
(94.9-114.6
BHBmg/dL5.21
(4.3-6.2
9.03
(7.6-10.4)
12.84
(11.0-14.7)
Calciummg/dL9.67
(9.59-9.79)
8.68
(8.37-8.99)
7.68
(7.16-8.20)
Glucosemg/dL60.2
(58.6-61.9)
56.9
(54.7-59.1)
>53.7
(50.9-56.4)
Magnesiummg/dL2.54
(2.49-2.60)
2.33
(2.23-2.42)
2.11
(1.97-2.24)
NEFAmEq/L0.299
(.263-.336)
0.454
(.397-.512)
0.609
(.531-.687)
NEFA:Chol. Ratiommol/L:mmol/L0.116
(.098-.134)
0.215
(.181-.250)
0.315
(.264-.365)
Urea Nmg/dL17.83
(16.9-18.8)
15.87
(14.5-17.2)
13.91
(12.2-15.6)

Reference:

Anderson, D. E. and Rings M. (2009) Current Veterinary Therapy: Food Animal Practice St. Louis, MO: Saunders Elsevier.

Blood tests from individual animals are routinely used to diagnose disease problems in dairy cattle. Veterinarians, producers and nutrition consultants alike seem to be interested in extracting pertinent information relative to herd nutrition and health status from blood tests. The Compton Metabolic Profile (CMP) has traditionally been used in this approach (10). The original intent of the CMP was to:

  1. monitor metabolic health of the herd;
  2. help diagnose metabolic problems and production diseases, and
  3. identify metabolically superior cows (10, 11). A "metabolic profile" is defined as a series of specific analytical tests run in combination and used as a diagnostic aid (7).
  • Use of metabolic profiles for the assessment of dietary adequacy in UK dairy herds. Macrae, A. I., D. A. Whitaker, et al. 2006. Vet Rec 159(20): 655-61.
  • The metabolic profile test: its practicability in assessing feeding management and periparturient diseases in high yielding commercial dairy herds. Kida, K. 2002 J Vet Med Sci 64(7): 557-63.
  • Metabolic profile in cows in the peripartum period with and without retained placenta. Kudlac, E., M. Sakour, et al. 1995. Vet Med (Praha) 40(7): 201-7.
  • Interpretation of pooled metabolic profiles for herd assessment. Davidek, J., Van Saun, R.J. Pp. 24, In: Oral and Poster Abstracts 25th Jubilee 25th World Buiatrics Congress, Budapest, Hungary, July 6-11, 2008.
  • Interpretation of Pooled Metabolic Profiles for Evaluating Transition Cow Health Status (Poster). Robert J. Van Saun, DVM, MS, PhD, Diplomate ACT and ACVN. Department of Veterinary Sciences, Pennsylvania State University

Take Home Messages

  • To assess a fresh cow problem, one should collect samples from far off dry, close-up dry, and fresh cows.
  • Pooled blood samples can be used to minimize costs.
  • Interpretation of pooled samples cannot be accomplished in the same method as with individual samples.

Introduction

Blood tests from individual animals are routinely used to diagnose disease problems in dairy cattle. Veterinarians, producers and nutrition consultants alike seem to be interested in extracting pertinent information relative to herd nutrition and health status from blood tests. The Compton Metabolic Profile (CMP) has traditionally been used in this approach (10). The original intent of the CMP was to:

  1. monitor metabolic health of the herd;
  2. help diagnose metabolic problems and production diseases, and
  3. identify metabolically superior cows (10, 11).

A "metabolic profile" is defined as a series of specific analytical tests run in combination and used as a diagnostic aid (7).

The CMP involved collecting 7-to-10 blood samples from 3 predefined groups of dairy animals, i.e., dry, peak lactation and midlactation, and having selected metabolites measured (10). From the test results, averages for each metabolite were calculated for each respective group and compared to reference values. Seven animals are considered the minimum number sampled to be statistically significant for interpretation. As one might expect completing 13 biochemical tests on 21 individual samples is extremely expensive ($200 to >$400 US), even with automated equipment.

The CMP had generally received positive endorsements as a diagnostic aid from studies outside the United States (2, 5, 10). In contrast, results of metabolic profiles in studies completed in the US have generally been less than enthusiastic about their potential diagnostic value (1, 8, 9). Application of this diagnostic procedure on a herd basis has been questioned relative to its validity and sensitivity in defining a problem as well as its total cost. Unfortunately in many herd situations, blood analyses are used preferentially in lieu of other more appropriate diagnostic procedures such as ration evaluation and physical exams and without regard for proper technique to ensure sound diagnostic information. However, blood metabolite analysis can reveal some useful information if properly interpreted in conjunction with animal and ration evaluations. The objective of this presentation will be to review the application and interpretation of a modified metabolic profile procedure for use in the diagnosis of metabolic problems associated with the transition dairy cow.

Nutrient Profile Analysis Procedure

The goal of any metabolite profiling is to obtain the "population" mean and determine dynamic changes over transitions. Larger sample size generally better represents the population. Initially, cost is the main deterrent to large animal numbers; however, why not pool samples since we are interested in the mean value and not individuals? Samples can be pooled by appropriate physiologic states to allow interpretation of dynamic changes in "population" means over a period of time. For example to address a fresh cow problem, pooled samples can be collected from recently dry cows (>7 days following dry-off), close-up dry cows (<2-3 weeks prior to calving), and fresh cows (< 45 DIM). Other appropriate sample pools can be determined given the specific problem to be addressed. By pooling samples you are obtaining information from a greater number of animals for much less cost. Rather than the standard 21 samples to calculate 3 group means, you may submit 3 pooled samples, which represent means of 10 to 20 animals each. The only negative part to this variation is the loss of statistical evaluation, i.e., population variance. However, this is not a major limitation if properly interpreted. Recent research has shown that pooled samples have the same value for most metabolites compared to means of individuals (13). Proper identification of appropriate animal groups or pools is absolutely critical if one is to obtain useful data.

For data from pooled samples to be relevant, all cows should be equally represented. Samples should be drawn only from visually normal animals to more accurately represent the population for a nutritional status evaluation. If needed, you could pool both clinical and nonclinical animals within a given group for comparison. To be able to appropriately interpret changes from one physiologic state to another at a single point in time, all animals should have been exposed to the same diets and management environments. This means to say that the fresh cows sampled today received the "same" diet that the early dry cows are currently receiving. If a dietary change was made recently, then comparisons between physiologic states may not be appropriate. If no changes were made, then compare dynamic changes in the "population" means for specific metabolites in accordance with clinical signs and ration evaluation.

Energy Balance Assessment

Energy balance is by and far one of the most critical nutritional factors impacting on animal health, lactation, and reproductive performance. Traditionally we have monitored changes in energy balance via body weight and condition score changes over time. This procedure may not be a sensitive enough tool when dealing with the transition cow. However, body condition score monitoring is still an important management tool, especially in assessing body condition changes with lactational performance. Another parameter that might be useful in assessing energy status is ketone body concentrations. At present measurement of beta-hydroxybutyrate (BOHB) concentration is most commonly used. However BOHB concentrations may not be sensitive enough and can come from dietary sources. A third method is a traditional research procedure, which has recently received much interest in the field. This is measurement of nonesterified fatty acids (NEFA) as a determination of energy balance. Many research studies have shown good correlations between energy balance and serum NEFA concentrations. Serum NEFA concentration is the result of adipose tissue breakdown of fat in response to negative energy balance. Circulating NEFAs are absorbed and metabolized for energy by the liver and other tissues. Concentration of NEFA then directly reflects the amount of adipose (fat) tissue breakdown taking place. Excessively high NEFA concentrations due to negative energy balance results in fatty infiltration of the liver, which is associated with higher incidence of periparturient metabolic diseases (3, 4, 6). Reference values for NEFAs are based on data from Michigan State University Clinical Nutrition Laboratory (Table 1). Clinical experience suggests serum NEFA concentrations to be more sensitive to energy balance changes compared to body condition scoring in transition cow situations.

Table 1. Suggested serum values for total cholesterol and nonesterified fatty acids (NEFA) in the periparturient dairy cow.

Serum MetaboliteEarly DryClose-up DryFresh Cow
Total Cholesterol, mg/dl> 80> 75> 100
NEFA, mEq/L< 0.325< 0.40<= 0.6

Protein Evaluation

Assessing protein status is a bit more difficult than energy balance. At present there is no single metabolite that can be measured, which directly reflects protein status. As a result, multiple parameters are needed to assess protein status including blood urea nitrogen (BUN), creatinine, total protein, albumin, and creatine kinase (Ck). Urea nitrogen concentrations are influenced by a wide variety of interrelated parameters including: dietary protein intake and rumen degradability; dietary amino acid composition; protein intake relative to requirement; liver and kidney function; muscle tissue breakdown; and dietary carbohydrate amount and rumen degradability. Creatinine is used to assess renal function and its impact on BUN values. Total protein and albumin reflect availability of protein and their concentration decline in the face of protein deficiency. However, this occurs over a period of time. Albumin has a relatively short half-life and can reflect protein deficiency problems over a period of a month or two. Creatine kinase is released from muscle when it is catabolized or injured. In most dietary protein deficiency situations, BUN values will be low (<10 mg/dl) with normal albumin concentration (>3.5 g/dl) in the early dry cows. Close-up dry cows will have low to moderate BUN, lower albumin and elevated Ck values. Fresh cows generally have low BUN and low albumin (<2.5 g/dl). These fresh cows seemingly fail to properly respond to any disease insult. Protein deficient fresh cows will die from metritis, mastitis, foot rot, and anything else without antibiotic therapy. An interpretation of this situation is that there are no amino acids available to support the immune system and it fails, predisposing the animal to any bug that comes along.

Liver Function Evaluation

We are all too familiar with the process of fatty infiltration of the liver in the transition cow. Much has been written on the negative role of excessive fatty infiltration and incidence of periparturient disease. Fatty infiltration of the liver is a natural process for the dairy cow transitioning into lactation, but it must be under control. Liver function can be assessed through a variety of enzymes (gamma-glutamyltransferase [GGT], aspartate aminotransferase [AST] and sorbitol dehydrogenase [SDH] and total bilirubin concentrations in the blood. Unfortunately, an elevation in any of these parameters does not mean anything more than some insult has occurred to the liver. Bilirubin values are most specific to bile flow problems than overt liver cell damage. These enzyme values need to be interpreted in conjunction with total cholesterol and NEFA results.

As described for energy balance, NEFAs are released into the circulation as a direct result of fat breakdown. The liver takes up NEFA in direct relationship with their concentration in blood. Once in the liver, NEFAs can either be partially metabolized to ketone bodies or distributed to other tissues for energy metabolism or they can be used to synthesize fat. High NEFA values result in either elevated ketones or fat production by the liver. Fat in the liver has two potential options, remain in the liver cell and initiate hepatic lipidosis (fatty liver) or be transported out of the liver. In order for fat to be transported out of the liver, protein is required. Fat is transported in blood in compounds termed lipoproteins; this is the only way they are soluble in blood. The lipoprotein structure that transports fat from the liver is identified as a very low density lipoprotein (VLDL). Associated with fat in the VLDL structure is a substantial amount of cholesterol. Therefore, total serum cholesterol indirectly measures the presence of VLDL in blood and consequently measures the liver's ability to produce VLDL. If VLDL production is compromised, hepatic fatty infiltration will ensue. Therefore the values described above represent total cholesterol values that characterize conditions in which VLDL production is limited and fatty infiltration is probable. Some investigators have suggested assessing the NEFA to Cholesterol ratio for this reason (6).

Macromineral Evaluation

Macrominerals calcium (Ca), phosphorus (P), potassium (K), magnesium (Mg), sodium (Na), chloride (Cl), and sulfur (S) are of extreme interest as to their status relative to their role in milk fever, alert downer cows, and weak cow syndrome. Unfortunately, most of these minerals are tightly regulated in the body through a variety of homeostatic processes. Blood concentrations of macrominerals are not reflective of dietary status when the homeostatic system is functioning properly. Phosphorus, K, Mg, and S are macrominerals in which blood concentrations are somewhat sensitive to dietary intake. Sodium and chloride concentrations are altered when renal or digestive function is compromised or in extreme dietary deficiency states. Assessment of Ca concentrations around the time of calving is a useful indicator of how well the Ca regulatory system is working and potential for clinical or subclinical hypocalcemia problems. Other than the 2 weeks prior to and following calving, blood Ca is not a very diagnostic value as a result of the intact regulatory system.

Therefore macromineral blood concentrations will need to carefully interpret in light of whether or not the homeostatic system is in proper operation.

In lieu of directly measuring macromineral blood concentration, measurements of parameters directly related to functioning and responsiveness of the homeostatic regulatory system may offer some insight as to nutritional status. Other methods of determining mineral balance such as urinary excretion patterns are being investigated as more sensitive indicators of nutritional status.

Micromineral and Vitamin Evaluation

Assessment of trace mineral and fat-soluble vitamin status is routinely completed using direct blood concentration measurements. The question to ask is whether or not there is a predictive relationship between tissue and blood trace mineral or fat-soluble vitamin concentrations and presence of nutrient-specific deficiency disease. On the surface one would have to say yes because we can document low nutrient concentrations in the presence of disease signs. The question really becomes one of how predictive mineral and vitamin concentrations are and which ones are the best indicators. To understand the issue here we need to appreciate that trace minerals and fat-soluble vitamins are not in a single large pool in the body, but are distributed into a number of different pools, which have different functions and availability. The different nutrient pools described include a storage, transport, and biochemical function pools (12). As a result of the storage capacity for trace minerals and fat-soluble vitamins in the liver, moderate dietary deficiencies or short-term severe deficiencies can be overcome without any effect on the critical biochemical functions performed by the element in question. If the dietary insult is severe or prolonged enough to drain the storage pool, then some effects might be seen in the transport pool. Finally when the transport pool has been compromised, the biochemical function pool will be compromised resulting in some dysfunction. It is only when the biochemical function pool reaches a critically low level that we see the overt clinical deficiency disease we learned about in textbooks. Before we reach the clinical disease stage, we will see problems associated with subclinical disease including increased disease susceptibility as a result of compromised immune function. This is the bulk of the trace mineral and fat-soluble vitamin deficiency disease problems.

The next issue to address is the ability of the chosen marker to be measured and its relationship to changes in one or more of these trace mineral or fat-soluble vitamin pools. Most of our markers currently being used are element concentrations in either whole blood or serum. These probably reflect the transport pool and not necessarily the biochemical function pool. As a result they may not be higher correlated with the presence of clinical disease. A good example here is serum copper concentrations. Unless serum copper is a critically low value, it has no significant predictive value in assessing potential for copper deficiency disease. Another example is the debate between serum and whole blood selenium values. Serum selenium values represent the transport pool and are very sensitive to dietary changes and liver mobilization. On the other hand, whole blood selenium values represent both transport and a portion of the biochemical function pools. This measure is somewhat less sensitive to dietary changes as a result of the greater proportion of whole blood selenium being present as the erythrocyte enzyme glutathione peroxidase. If one were to assess a potential response to a dietary change, serum selenium values would respond within a day or two while whole blood may take a month or more to show a significant change. This could dramatically impact on your interpretation of the dietary response.

Liver mineral concentrations are good markers for the storage pool; however, they are not always highly associated with the presence of disease. Liver mineral concentrations may give us some insight into the adequacy of the mineral program and potential for disease. One additional avenue here is the assessment of mineral status in fetal and neonatal animals. Research has shown that the fetus can concentrate trace minerals in its liver and therefore, comparison to adult values is inappropriate. Secondly, fetal liver has a lower dry matter content than the maternal liver further substantiating the inability for direct comparison. Databases determining normal trace mineral concentrations in the fetal and neonatal liver need to be developed. Obviously we are a long way away from accurately predicting the potential presence of trace mineral deficiency disease problems with our current methodologies. A number of more predictive markers for specific nutrient pools need to be identified.

Metabolic Profiles: Interpretation of Results

For individual animals, metabolite values are compared to standard, laboratory-dependent reference values. These reference values generally represent a 95% confidence interval. This means that 95% of normal animals should have a given metabolite concentration within this range. This also suggests that 5% of the population will be outside of this reference range and still be normal, emphasizing the need to clinically evaluate the animal. A number of factors, most notably physiologic state and age have been shown to influence blood metabolite concentrations. Most reference ranges do not account for these differences and thus may confound direct interpretation. Having a thorough understanding of the physiologic regulation of a given nutrient is crucial to interpretation. For trace minerals, blood or serum concentrations are buffered from acute changes as a result of dietary problems through mobilization of storage mineral, usually from the liver. This suggests that liver trace mineral status may be a better indicator of dietary adequacy, whereas measurement of a mineral-specific enzyme activity better reflects the presence of overt clinical deficiency disease compared to blood concentrations. Many trace mineral concentrations in blood are influenced by disease. As we come to better understand the factors that affect metabolites, we can adjust and better assess nutritional status.

In contrast to individual animal samples, pooled mean metabolite values can not be directly compared to reference ranges in the same way. When interpreting pooled samples one needs to remember that measured value represents a population with individuals above and below the mean. As a general rule, means of pooled samples should be near the midpoint of the reference range to be considered normal. For example, if serum total calcium (Ca) concentration for fresh cows is 9 mg/dl and the reference range is 9 to 12 mg/dl, this might be interpreted to suggest a potential problem with subclinical hypocalcemia whereas it would be considered normal in an individual. The measured mean of 9 mg/dl represents a population with approximately 50% of the individual values above and below. This suggests that a number of individuals would have serum Ca concentrations below the normal range. Of course interpretation of metabolic profile results has to be considered in light of presenting problems in the herd. If the herd is experiencing clinical signs consistent with subclinical hypocalcemia, e.g., slow increase in feed intake and milk production, displaced abomasum and ketosis problems, this would be supportive evidence of the metabolic profile results.

Without population variance determinations, you cannot really determine how significant mean differences are. Yet, with many metabolites, like BUN, Ca, Mg or glucose, you can eliminate the possibility that a single sample was sufficiently low or high to skew the mean. For low BUN values, it is difficult to have values approaching zero whereas for other metabolites, if the sampled cow had an extremely skewed value, it would have been exhibiting clinical signs and would not have been sampled. Metabolites with high variability (wide range of values) will be of less diagnostic value as compared to low variability metabolites (Table 2).

Table 2. Categorization of blood metabolites relative to their range of values (variability) and diagnostic value.

Low Variability High Diagnostic ValueModerate Variability and Diagnostic ValueHigh Variability Low Diagnostic Value
Albumin, Total proteinCholesterolCreatine kinase
Calcium, Phosphorus, MagnesiumBUNLiver enzymes
Sodium, Chloride, PotassiumGlucose
NEFAKetones

Summary

Traditional metabolic profiling of the dairy herd resulted in tremendous financial investment with subsequent unsatisfactory results in many situations. A variety of factors are responsible for individual and herd variation in blood metabolite concentrations confounding interpretation. In addition, the cow has an exquisite system of checks and balances, which maintains normal physiologic function within a wide array of dietary and environment insults. As a result of these physiologic regulatory mechanisms, simple blood concentration analysis has not been highly rewarding in accurately assessing nutritional and fertility status. A new approach to metabolic profiling, which involves pooling larger sample numbers, specific animal selection relative to physiologic state and stage of lactation, has been examined in an effort to better interpret serum metabolite concentrations on a herd basis. Most importantly it must be remembered that metabolic profiles are almost useless without being coupled with animal and facility evaluations, body condition scoring and ration evaluation. The combination used within a team approach can be an extremely useful diagnostic tool in nutritional evaluations of the dairy herd. It is only when the whole picture is evaluated will the uses of metabolic profiles produce useful diagnostic information.

References

  1. Adams, R.S., W.L. Stout, D.C. Kradel, et al. (1978). Use and limitations of profiles in assessing health or nutritional status of dairy herds. J Dairy Sci 61:1671.
  2. Bogin, E.Y. Avidar, M. Davidson, et al. (1982). Effect of nutrition on fertility and blood composition in the milk cow. J Dairy Res 49:13-23.
  3. Cameron, R.E.B., P.B. Dyk, T.H. Herdt, et al. (1998). Dry cow diet, management, and energy balance as risk factors for displaced abomasum in high producing dairy herds. J Dairy Sci 81:132-139.
  4. Dyk, P.B., R.S. Emery, J.L. Liesman, et al. (1995). Prepartum nonesterified fatty acids in plasma are higher in cows developing periparturient health problems. J Dairy Sci 78(Suppl. 1):264, Abstr.
  5. Ghergariu, S., G.J. Rowlands, A. Pop, et al. (1984). A comparative study of metabolic profiles obtained in dairy herds in Romania. Br Vet J 140:600-608.
  6. Holtenius, P., and M. Hjort. (1990). Studies on the pathogenesis of fatty liver in cows. Bovine Practitioner 25:91.
  7. Ingraham, R.H. and L.C. Kappel. (1988). Metabolic profile testing. Veterinary Clinics of North America:Food Animal Practice 4(2):391.
  8. Jones, G.M., E.E. Wildman, H.F. Troutt, et al. (1982). Metabolic profiles in Virginia dairy herds of different milk yields. J Dairy Sci 65:683-688.
  9. Lee, A.J., A.R. Twardock, R.H. Bubar, et al. (1978). Blood metabolic profiles: Their use and relation to nutritional status of dairy cows. J Dairy Sci 61:1652.
  10. Payne, J.M., S.M. Dew, R. Manston, et al. (1970). The use of a metabolic profile test in dairy herds. Vet Rec 87:150.
  11. Rowlands, G.J., J.M. Payne, S.M. Dew et al. (1973). A potential use of metabolic profiles in the selection of superior cattle. Vet Rec 93(2):48-49.
  12. Suttle, N.F. (1986). Problems in the diagnosis and anticipation of trace element deficiencies in grazing livestock. Vet Rec 119:148-152.
  13. Tornquist, S.J. and R.J. Van Saun. (1999). Comparison of biochemical parameters in individual and pooled bovine sera. Veterinary Pathology 36(5):487.

Analyte reference values should represent the population mean (or median if not normally distributed) and variation from a defined population of animals clinically evaluated to be free of disease and other health problems and fed an appropriate diet. Reference values for each metabolite need to be refined to minimize inherent variability due to effects of age, physiologic state, production level, and other cow-specific factors on analyte concentration and improve sensitivity of analyte to environmental (i.e., nutritional) influences. At present, few laboratories have specialized blood analyte reference criteria that are adjusted for age, physiologic state, and time relative to calving effects. Research is currently underway to develop appropriate metabolic profiling reference criteria.

Threshold or cut point criteria are derived from statistical modeling using logistic regression and calculating odds ratios or relative risk. In this process, the prevalence of a specific disease (i.e., retained placenta, ketosis, metritis, etc.) or any disease event is related to various concentrations of a specific analyte to determine if significant predictive relationships exist. For example, fresh cows with serum BHB concentrations ≥ 12.5 mg/dL (1200 µmol/L) were 8 times more likely to experience a left displaced abomasum. A number of studies have defined disease risk relationships to various blood analyte concentrations.

Expected analyte concentrations for healthy periparturient mature dairy cows are presented in Tables 3 and 4. Standards for defining appropriate reference values for metabolic profiling have been suggested. Greater clinical adoption of metabolic profiling testing is predicated upon development of robust reference criteria to improve diagnostic interpretation.

Serum or plasma concentrations of NEFA and BHB have been the most studied in the periparturient dairy cow. Higher NEFA concentrations in either the close-up dry (≥ 0.4 mEq/L) or fresh (≥ 0.6 mEq/L) period are associated with increased risk for many periparturient diseases. Prepartum BHB concentrations are not predictive of disease, but postpartum concentrations are very sensitive indicators of disease risk. Subclinical ketosis diagnosis has been defined by BHB concentrations of 12.5 or 14.5 mg/dL (1200 or 1400 μmol/L). However, BHB concentrations of 10 mg/dL (0.96 mmol/L) and greater are associated with increased risk of a cow experiencing some postpartum disease.

Other blood analytes have also been shown to be predictive of disease risk. Albumin concentrations in the close-up (≤ 3.25 g/dL) and fresh (≤ 3.4 g/dL) periods have been associated with increased disease risk. Low total protein (≤ 6 g/dL) in fresh cows has also been suggested to indicate disease risk concerns, though hypergammaglobulinemia resulting from inflammatory response can confound total protein interpretations. Calculated NEFA-to-Cholesterol ratio was suggested as an index of liver function and indicator of hepatic lipidosis. The ratio is calculated with both measures expressed on a mmol/L basis (mg/dL cholesterol x 0.02586 = mmol/L). Cows with increased NEFA:Cholesterol ratio in the close-up dry (≥ 0.2) and fresh (≥ 0.3) periods were are greater risk for postpartum disease. Other analytes did not show significant disease risk associations, but cows collectively experiencing postpartum disease had lower albumin, UN, glucose, and cholesterol and higher NEFA, BHB, NEFA:Cholesterol ratio, and AST concentrations compared to healthy cows.

Table 1. Expected range (95% confidence interval) in variouis blood analyte concentrations over the periparturient period for healthy, mature dairy cows. (Close-up dry is defined as -3 to -21 days prior to calving; Fresh cows defined as 3 to 30 days in milk.)
AnalyteUnitsClose-up DryFresh
Albumin*g/dl3.3 - 3.73.2 - 3.6
Aspartate Aminotransferase (AST)IU/L46.5 - 82.661.1 - 103.0
Beta-Hydroxybutyrate (BHB)*mg/dL1.25 - 4.21.7 - 8.9
Cholesterolmg/dL65 - 11463 - 253
Glucosemg/dL51 - 7442 - 68
Nonesterified Fatty Acids (NEFA)*mEq/L0.03 - 0.460.01 - 0.52
Total Proteing/dL6.9 - 8.57.3 - 8.9
NEFA: Cholesterol*Ratio0.03 - 0.200.03 - 0.40
Table 2. Fresh cow mineral concentrations in healthy population and concentrations that are of concern for potential disease risk.
AnalyteAdequate RangeConcern Levels
Calcium*8.7 - 11.0 mg/dL

(2.17 - 2.74 mmol/L)
< 8 mg/dL

(<2.0 mmol/L)
Phosphorus4.5 - 8.0 mg/dL

(1.45 - 2.58 mmol/L)
< 3.5 mg/dL

(<1.13 mmol/L)
Magnesium*2.0 - 3.5 mg/dL

(0.82 - 1.43 mmol/L)
<1.5 mg/dL

(<0.62 mmol/L)
Sodium*137 - 148 mEq/L< 137 mEq/L
Potassium*3.8 - 5.2 mEq/L< 3.0 or > 5.5 mEq/L
Copper0.6 - 1.5 mg/mL

(9.4 - 23.6 mmol/L)
<0.45 or > 4 mg/ml

(<7.1 or >63 mmol/L)
Iron130 - 250 mg/dL

(23.3 - 44.9 mmol/L)
< 130 or > 1800 mg/dL

(<23 or > 322 mmol/L)
Zinc0.8 - 1.4 mg/ml

(0.89 - 1.3 mmol/L)
< 0.5 or > 3 mg/ml

(<7.6 or > 45.9 mmol/L)
Selenium, serum70 - 100 ng/ml

(0. 89 - 1.3 mmol/L)
< 35 or > 800 ng/ml

(<0.44 or >10.1 mmol/L)
Selenium, whole blood120 - 250 ng/ml

(1.5 - 3.2 mmol/L)
<50 or > 1900 ng/ml

(<0.63 or > 24 mmol/L)
Serum Vitamin A*225 - 500 ng/ml< 150 ng/ml
Serum Vitamin E*3 - 10 ug/mL< 3.0 ug/ml
Vitamin E:Cholesterol Ratio2.5:6.0<1.5

*Analyte has been shown in one ore more studies to be predictive for disease risk.
Reference: Anderson, D. E. and Rings M. (2009) Current Veterinary Therapy: Food Animal Practice St. Louis, MO: Saunders Elsevier.

Pertinent References from Bibliography

  • Metabolic profile testing for Jersey cows in Louisiana: reference values. Roussel, J. D., S. H. Seybt. et al. 1982. Am J Vet Res 43(6): 1075-7.
  • Metabolic Profile Testing in Dairy Herds: Wrong Answer or Wrong Question? Eicher, R. Acta Veterinaria Scandinavica 2003, 44(Suppl 1) p28.

Chemistry units

AnalyteUS unitsSI unitsconversion factors
to SI units
conversion factors
to US units
Chemistry
ALT, SGPT IU/L U/L 1 1
Albumin g/dL g/L 10 0.1
Alkaline Phosphatase IU/L U/L
1 1
Ammonia
umol/L umol/L 1 1
Ammonia
ug/dL umol/L 0.5872 1.703
Amylase IU/L U/L
1 1
AST, SGOT IU/L U/L 1 1
Bicarbonate mEq/L mmol/L 1 1
Bilirubin, total
mg/dL umol/L 17.1 0.058
Calcium mg/dL mmol/L 0.25 4
Calcium mmol/L mmol/L 1 1
Chloride mEq/L mmol/L 1 1
Cholesterol
mg/dL mmol/L 0.02586 38.7
CO2 mEq/L mmol/L 1 1
Creatinine
mg/dL umol/L 88.4 0.011
Gamma glutamyl transpeptidase
IU/L U/L 1 1
Globulins g/dL g/L 100.0
Glucose mg/dL mmol/L 0.05551 18
Lactic acid mmol/L mmol/L 1 1
Lipase IU/L U/L 1 1
Magnesium mg/dL mmol/L 0.411 2.433
Magnesium mEq/L mmol/L 0.5 2
Osmolality
mOsm/kg mmol/kg 1 1
Oxygen, partial pressure (PaO2) mmHg kPa 0.1333 7.5
pH pH units pH units 1 1
Phosphorus mg/dL mmol/L 0.3229 3.1
Potassium mEq/dL mmol/L 1 1
Protein g/dL g/L 10 0.1
Sodium
mEq/dL mmol/L 1 1
Triglycerides mg/dL mmol/L 0.01129 88.57
BUN mg/dL mmol/L 0.357 2.8
Endocrine chemistry
ACTH pg/mL pmol/L 0.2202 4.541
Cortisol
ug/dL nmol/L 27.59 0.036
Erythropoietin mIU/mL U/L 1 1
Gastrin ng/L
ng/L 1 1
Growth hormone ng/mL ug/L 1 1
Insulin uIU/mL pmol/L 7.175 0.139
Parathyroid hormone (PTH)
pg/mL ng/L 1 1
Prolactin ng/mL ug/L 1 1
Renin activity
ng/mL/hr ng/(L sec) 0.2778 3.6
Thyroid-stimulating hormone (TSH) uIU/mL mU/L 1 1
Thyroxine (free) ng/dL pmol/L 12.87 0.078
Thyroxine, total (T4) ug/dL nmol/L 12.87 0.078
Triiodothyronine, total (T3) ng/dL nmol/L 0.01536 65.1
Immunology
Immunoglobulin (IgA,G,M)
mg/dL g/L 0.01 100
Immunoglobulin (IgE) IU/mL ug/L 2.4 0.42
Hematology and coagulation
Bleeding time
min min 1 1
Differential blood count % fraction of % 0.01 100
Erythrocyte count
x10^6/uL x10^12/L 1 1
Folate (folic acid) ng/mL nmol/L 2.266 0.44
Hematocrit % fraction of % 0.01 100
Hemoglobin g/dL mmol/L 0.6206 1.6
Iron (Fe) ug/dL umol/L 0.1791 5.6
Iron binding capacity ug/dL umol/L 0.1791 5.6
Leukocyte count x10^3/uL x10^9/L 1 1
Mean corpuscular hemoglobin (MCH) pg/cell pg/cell 1 1
Mean corpuscular hemoglobin conc (MCHC)
g/dL g/L 10 0.1
Mean corpuscular volume (MCV) um^3 fL 1 1
Partial thromboplastin time, activated sec sec 1 1
Platelet count x10^3/uL x10^9/L 1 1
Platelet, mean volume
um^3fL
1 1
Prothrombin time
sec sec 1 1
Reticulocyte count
% red cellsfraction of %0.01100
Vitamin B12pg/mLpmol/L0.73781.36
von Willebrand factor (vWF) antigen%fraction of %0.01100