Resources Learn more about ELISA technology and measuring biomarkers in different application areas such as pancreatic, NAFLD/NASH, cardiovascular and obesity research. Sandwich technology ELISA (Enzyme-linked immunosorbent assay) is a widely used biochemical technique for the detection of an antigen in a sample. The sandwich ELISA utilizes two antigen specific antibodies; one capture antibody bound to a solid phase and one enzyme-linked detection antibody. Direct enzyme conjugation of the detection antibody ensures an easy-to-use and sensitive assay with minimal background signal. Sequence assay – Antigen and detection antibody are added stepwise. In a sequence assay, sample is applied to an antibody-coated microtiter plate well. The capture antibody binds to the antigen in the sample. Unbound compounds are removed by a washing procedure before the enzyme-linked detection antibody is added to the well. The detection antibody binds to a second epitope (immunogenic part) on the antigen. Unbound antibodies are removed by washing before the addition of a substrate, which is converted by the enzyme to a chromogenic signal. The enzyme reaction is stopped and the result is monitored spectrophotometrically. The more antigen in the sample, the stronger the signal. Simultaneous assay – Antigen and detection antibody are added simultaneously. The sandwich ELISA may also be designed as a simultaneous assay where the sample and detection antibody are added in the same step. When accuracy matters When analyzing samples in the ELISA platform, there are a variety of components that will affect the test results. A highly specific assay based on top-quality reagents with a low assay variation will give a test result close to the true value. Precision The intra(within) assay variation is dependent on the properties of the assay. A poorly optimized assay with low sensitivity will give high intra assay variations. The inter (between) assay variation is mainly dependent on the robustness of the assay. An ELISA sensitive to changes in the assay environment or production conditions will contribute to a high inter assay variation. Both the intra and inter assay variations are dependent on user handling and performance of the assay. The variation in the ELISA format is often expressed in terms of Coefficient of Variation (%CV), which is a relative standard deviation measure. The reproducibility between assays should be monitored by the use of controls. Analyzing controls along with unknown samples gives reliable evaluations and conclusions, even if the samples have been analyzed at different times. Specificity There are substances in serum and plasma samples that may affect the interaction between the antibodies and antigen in the ELISA. These substances can be complement, rheumatoid factors or heterofilic antibodies. It is important to block these proteins from interfering with the assay. Spiking studies or sample dilutions show if there are any problems with interfering agents. Other specificity issues could potentially occur if there are substances in the sample that are similar to the analyte of interest. Such homologous proteins must be considered during assay development in order to try to avoid their impact on the result. A specific assay will give results close to the true value. Assay sensitivity Sensitivity is a statistical term used to describe the ability of an assay to separate the blank from the specific analyte. Since there are a variety of methods used for determining sensitivity, the method must be known when comparing sensitivity between assays. Capability of detection ISO11843-Part 4 describes one method for the determination of the Capability of Detection (CoD) and reflects the minimum level that is clearly distinguishable from Calibrator 0. Capability of Detection should be seen as a method of validation, rather than the lowest concentration that can be measured. Lower limit of quantification (LLOQ) Lower Limit of Quantification (LLOQ) is defined according to the FDA Guidance for Industry, Bioanalytical Method Validation, May 2018, as the lowest concentration of an analyte in a sample that can be determined with acceptable precision and accuracy,. The acceptance criteria are ≤ 25% for total variation and 100±25% for accuracy. Hook effect The high dose hook effect refers to measured levels of antigen displaying a significantly lower absorbance than the actual level present in a sample. This appears when a simultaneous ELISA assay is saturated by a very high concentration of sample antigen binding to all available sites on both the solid phase antibody as well as the detection antibody and thereby preventing the sandwich-formation. The antigen-saturated detection antibodies in solution will be washed off giving a falsely low signal. A “hook” is observed in the curve when data is plotted as a signal versus antigen concentration. A high dose hook is observed in the plotted curve when a simultaneous assay is saturated by high antigen concentrations. Calculations of results Please read the Directions for Use for product-specific recommendations. There are several types of curve-fitting methods. They can be divided into two categories: linear and non-linear. The choice depends on the shape of the measuring range for the particular assay. Both FDA and EMA recognize that most ligand-binding assays do not have a linear measuring range but often a sigmodial appearance, thus the model used should be non-linear. When calculating results, the Blank/Calibrator 0 should be seen as a negative control and not be included in the calibration curve. Cubic spline The Cubic Spline Regression analysis is a non-linear algorithm that is one of the models recommended by Mercodia. The Spline method is a piecewise third order polynomial, which has knots at every calibrator concentration. The whole function and its first and second derivates are continuous. There are two kinds of Spline fitting methods: – Smoothed Spline – Interpolation Spline If available, Smoothed Spline is the best method. It selects the degree of smoothing so that it reaches the optimal for a continuous function. An outlying calibrator value within the calibrator curve will not have an effect on the curve fitting. The curve fitting in the Interpolation Spline method will draw the calibrator curves exactly through all calibrator responses, which could risk creating a function where sample results cannot be evaluated. Logistic Regression Logistic regression is another non-linear algorithm recommended by Mercodia. It is well suited for use in ligand-biding assays because it can describe sigmoidal relationships accurately. Four parameter logistic (4PL) The algoritm will estimate four parameters in order to fit the curve. The model fits data that makes an S-shaped curve. Five parameter logistic (5PL) The model can handle assymtric relationships by adding one parameter compared to the 4PL algoritm. As the name implies five parameters will be estiamted in order to fit the curve. Depending on the iterative fitting process used by the software and the ability to fit the algorithm to the actual data generated, a weighting function can be added. By doing this the curve will easier converge to the data, even if outlying values are present. Manual calculation If a curve-fitting program is not available, calculations can be done with pen and paper or by using Excel. Mercodia provides Excel Evaluation Sheets upon request. These sheets use a simple approximation (interpolation polynomial) to mimic a spline function and can be used for most products. Further reading: Findlay et al (2007) Establishment of control ranges and target values The routine analysis of controls with known concentrations is a valuable tool for the acceptance of test runs and to validate that analyzed sample concentrations are in fact repeatable and reproducible at your laboratory. Mercodia can assist you to establish a control program, with target values and acceptance ranges for your control material. 1. Pool samples (serum or plasma) with low, intermediate and high analyte levels. 2. Aliquot each control level and store in -20°C. 3. Run each control level at 12-20 separate occasions. Preferably by different operators and with different batches of the method for all error factors to be included. Run in as many replicates (for example duplicates or triplicates) as you intend to run your samples. 4. Determine the mean (target) value and intra, inter and total assay variations (SD) for example by a one-way ANOVA. 5. Establish the range from the total variation (mean +/- SD). When monitoring controls, these ranges are used to verify if a run is accurate. If your control values are within the established acceptance range your test run is considered accurate. You may also send your results from your control measurements to Mercodia and we will help you establishing control ranges and target values. Contact us Thoughts from the expert Hanna Ritzén is the Managing Director Research and Development at Mercodia AB. Today, she is responsible for the product development of commercial IVD and RUO ELISA kits and post market surveillance, as well as the assay development and validation and sample analysis in the bioanalytical services. She is also involved in the clinical laboratory test standardization and harmonization of insulin and C-peptide biomarker methods administered by the American Association of Clinical Chemistry. What are your current research focuses? “At our department we primarily focus on developing new and existing ELISA kits for biomarker detection, as well as validating sponsor specific and other commercially available assays to meet the needs of our bioanalytical service customers. We put our emphasis on biomarker detection assays in the areas of metabolic disease and offer assays and services applicable to both human and animal models. The US Food and Drug Administration (FDA) has identified biomarker development as one of a few areas with greatest potential impact, and we believe that this field will be increasingly important in clinical diagnostics as well as in drug development.” What are the challenges you face with validating bioanalytical methods for biomarkers? “There are a number of commercial assays for determination of biomarkers from numerous vendors, but the extent of method development and critical reagent performance characterization may vary significantly and can, in many cases, be insufficient to support the use of these kits in drug development and for conducting analyses of samples from clinical trials. Today, there are no guidelines for the validation of biomarker assays for drug development. For biomarkers with endogenous levels it is difficult to perform all validations in a correct way. You have to look at relative accuracy, because no blanc matrix exists, and use both endogenous and synthetic protein to validate your assay. To be able to compare with real values you have to look at both accuracy and precision. To compare different methods, you have to have a reliable reference method and well-characterized reference preparation that is commutable between methods, as well as extensive knowledge about the sources of errors for each method. As an example, The Mercodia Insulin ELISA, available on the market for more than 20 years, has been re-validated for intended use in drug development programs in relation to FDA, EMA and CLSI guidelines. A biomarker validation in relation to FDA and EMA guidelines puts high demands on the validation design, given the aim of the guidelines is mainly to guide PK studies and not necessarily to guide the validation of biomarkers. Native insulin or insulin analogues are present in all donor matrices. In addition, the use and/or development of insulin analogues pose a special challenge of specificity given the similarity between molecules. This adds additional demands on the assay validation as it must be designed to show if the method is fit–for-purpose or not. Before starting a biomarker research program, it is very valuable to develop a bioanalytical strategy, where you start with defining the specific needs of the project and the indented use of the data. A successful bioanalytical strategy also involves engaging of stakeholders, with documentation and kit selection and investigations on assay performance and sample analysis. The strategy should combine scientific rationale, analytical performance and regulatory expectations and will ensure that the biomarker assay chosen is fit-for-purpose for the context of use.” How can the field be regulated and standardized? “Since commercial immunoassay kits are used increasingly to support drug discovery and development, their characterization has to be standardized and this has to be a joint responsibility for regulatory bodies, scientists developing assays and performing sample analysis as well as clinicians. The appropriate interpretation of measurement data ultimately relies on the expertise of scientists and technicians and on the quality of the measurement method. I believe that producers, regulatory organisations and clinicians/laboratories have to work together to: • Develop robust methods • Optimize methods that gives accurate results with low analytical error • Verify and validate methods • Follow and establish guidelines • Establish allowable errors • Evaluate the analytical errors vs medically allowable errors • Use and validate appropriate methods If we all can work together to increase measurement quality, we can save time and sample volume by reducing the number of reruns and fewer samples and subjects will be needed to reach statistical significance. The results will be consistent between testing sites, contributing to higher efficiency in the drug development process, thus contributing to improved patient outcome and lowering the cost of drug development.” Where do you hope this field will be in 5–10 years’ time? “I hope that all stakeholders within biomarker research will join responsibility for biomarker validation and see each other as partners rather than competitors. We need to connect needs and knowledge from clinical diagnostics and drug discovery. When both acceptance criteria as well as how a study should be performed is clearly defined, we can all trust that the assays used for the analysis of biomarkers will give us reliable results. We are all dealing with the same thing, analysis of biomarkers, but in different settings and I think we have a lot to learn from our different experiences. By bridging our knowledge we can work together towards a better understanding of biomarkers and their role in disease as well as in drug development.” Alpha-cell biomarker Glucagon In response to peripheral cues as well as signaling from neighbors, glucagon is released from alpha cells. Its primary role is to stimulate hepatic glucose production and it does so by activating glycogenolysis and gluconeogenesis, as well as inhibiting glycogenesis and glycolysis. Through this primary action, glucagon can prevent or rescue hypoglycemia. However, balance in this system is extremely important and glucagon must be regulated so as not to cause dangerously elevated levels of endogenous glucose production. See our human Glucagon ELISA See our animal Glucagon ELISA How to choose your glucagon assay Is high sensitivity important for glucagon measurements? To avoid wasting money on an assay that doesn’t fit your needs, make sure to ask about the functional sensitivity of the assay before purchasing. Many assays have a measurement range set much broader than the assay can measure with any kind of accuracy, and the lower limit at which the assay can provide quantitative results (LLOQ) is, in reality, much higher than what is stated. The Mercodia Glucagon ELISAs have superior sensitivity, which enables you to measure physiologically suppressed levels of glucagon. This has been proven by independent evaluators, stating that only the Mercodia Glucagon ELISA, when compared to Millipore and Meso Scale Discovery assays, had optimal performance for measuring glucagon concentrations in clinical samples (Wewer Albrechtsen et al., 2016). “Of further concern is the finding that some of the kits that we purchased appeared to be incapable of quantifying what they purported to.” (Wewer Albrechtsen et al., 2016) In order to provide the scientific community with the ability to detect glucagon under various physiological conditions, Mercodia offers two glucagon assays with superior measuring ranges, compared with other assays on the market. The Mercodia Glucagon ELISAs for human and animal samples have sensitivities of 1.5 pmol/L (5 pg/mL) and 2 pmol/L (7 pg/mL), respectively. These assays meet the needs of researchers as discussed by Bak et al., who state that test methods with sensitivities >5 pmol/L (17 pg/mL) do not allow for full characterization of glucagon (Bak MJ et al., 2014). The Mercodia Glucagon ELISA is validated according to FDA/EMA guidelines and has a full bioanalytical validation report that can be acquired from Mercodia. Am I getting accurate glucagon values? When choosing a glucagon immunoassay, make sure to take a few minutes and study the cross-reactivity table carefully. If you are interested in measuring active glucagon (1-29), the assay should not cross-react with the metabolite, glucagon 3-29 (Campbell & Drucker, 2015). Any cross-reactivity to the proglucagon-derived gut peptides oxyntomodulin and glicentin should also be low and if present, well described, because they contain the full glucagon sequence. Glicentin circulates at much higher levels than glucagon, and therefore cross-reactivity to glicentin can create serious measurement error when trying to measure glucagon. The Mercodia Glucagon ELISA has very low cross-reactivity to oxyntomodulin and glicentin, and data on the cross-reactivity at different concentrations can be obtained from Mercodia. Biacore and ELISA analyses have shown that there is no cross-reactivity to glucagon 3-29, at or above physiological concentrations, in the Mercodia Glucagon ELISA. This is an important finding since glucagon 3-29 has been reported to be the major metabolite in clinical samples due, in large part, to in vitro plasma protease metabolism during sample storage (Howard JW et al., 2015). Increased specificity is a primary reason why concentrations in the Mercodia glucagon assays are lower than concentrations generated by other glucagon assays. Issues with specificity raise questions about true glucagon concentrations reported by assays that cross-react with glicentin or for which the manufacturer chooses not to report cross-reactivity to relevant homologous proteins. The use of an assay that cross-reacts with other glucagon-containing proteins may result in inaccurate characterization of alpha cell function and glucagon kinetics under various physiological conditions, as well as misinterpretation of how a subject is responding to therapy. Falsely elevated levels will also have a negative impact on decisions about inclusion of subjects in studies or on stratifications based on specific glucagon cut-off values. If you work with samples from gastric bypass surgery patients or if you infuse high levels of gut hormones, contact Mercodia so that we can provide you with an alternative protocol for the Glucagon ELISA, which will result in even further increased specificity! Don’t forget to also pay attention to how cross-reactivity studies have been performed. Always check to see what concentrations were used in a manufacturer’s cross-reactivity testing, since the level of cross-reactivity is usually positively correlated to the concentrations tested. If levels are not stated, be suspicious! My sample volumes are limited – is there an assay that can meet my needs? The amino acid sequence of glucagon is highly conserved across species, but validation of different sample types is crucial because sequence homology is not the only factor that affects the antibody-antigen binding in an immunoassay. For example, matrix interferences (common in animal samples) can lead to falsely elevated or falsely low concentrations. According to Bak et al., some assays in their evaluation of human glucagon ELISAs from 2014 had poor recovery in plasma and/or buffer. The assays in this evaluation reported variable baseline concentrations and glucagon was not always detected, particularly under conditions in which it was suppressed. Mercodia assays contain a unique blocking solution to prevent or minimize matrix interferences, especially important for the 10 μL assay, which has been optimized for animal samples. Both of the Mercodia Glucagon assays can be used with samples from cultured cells, providing accurate glucagon measurement options for those conducting in vitro research (e.g., conversion of alpha to beta cells or vice versa, human embryonic stem cells to alpha or beta cells, production of islet-like clusters, etc.). Can I trust my results using different batches of the assay? Many assays produced with insufficient production methods will have edge effects and lack of homogeneity across the plate, giving rise to high variation between samples depending on plate position. Always check plate homogeneity with a sample in the lower segment of your measuring range (this is where this problem becomes the most apparent) to make sure the assay fits your purpose. There are many factors that could potentially influence the results from an immunoassay. It is extremely important to control every step of the manufacturing process, from antibody to final product, and this is why Mercodia only uses monoclonal antibodies. For the Mercodia Glucagon assays, two highly specific mouse monoclonal antibodies were developed and characterized extensively using ELISA and Biacore techniques. The 25uL Mercodia Glucagon ELISA is validated for human samples and the 10 μL Mercodia Glucagon ELISA has been validated for mouse, rat, and non-human primate samples. The antibodies are controlled and manufactured with the same characteristics in each lot, resulting in assays with very low lot-to-lot variation, year after year. References Bak MJ et al. (2014) Specificity and Sensitivity of Commercially Available Assays for Glucagon and Oxyntomodulin Measurement in Humans. Eur J Endocrinol 170:529-538. Campbell, J. E., & Drucker, D. J. (2015). Islet α cells and glucagon—critical regulators of energy homeostasis. Nature Reviews Endocrinology, 11(6), 329–338. Howard JW et al. (2015) Identification of Plasma Protease Derived Metabolites of Glucagon and Their Formation Under Typical Laboratory Sample Handling Conditions. Rapid Commun Mass Spectrom 29:171-181. Wewer Albrechtsen NJ et al. (2016) Dynamics of glucagon secretion in mice and rats revealed using a validated sandwich ELISA for small sample volumes. Am J Physiol Endocrinol Metab 311:E302-E309. Read more in our White Paper on Glucagon measurement Beta-cell biomarkers Proinsulin Preproinsulin is biosynthesized in the beta-cell through the secretory pathway, cleaved into proinsulin in the ER and then transported to the Golgi apparatus. Proinsulin processing occurs in maturing secretory granules and insulin and C-peptide are secreted simultaneously in equimolar amounts, in response to various stimuli, including glucose. See our proinsulin ELISA Insulin Insulin is made up of 51 amino acids and is one of the smallest proteins in the body. It is comprised of two polypeptide chains linked by two disulfide bonds, connecting cysteine molecules. There is a third disulfide bond that connects two amino acids within Chain A. Chain A consists of 21 amino acids and chain B contains 30 amino acids. The amino acid sequence of insulin is highly conserved among vertebrates. Virtually all primates and monkeys share the same sequence. Rat and mouse insulins only differ from primate insulin by 4 amino acids. Even the positions of the three disulfide bonds are the same for most species. The similarities in amino acid sequences and conserved positions of the disulfide bonds lead to a three dimensional conformation of insulin that is very similar across species. Thus, insulin from one species is often biologically active in other species. For example, pig insulin has historically been used to treat human patients. Insulin from some species of fish can also be similar enough to human insulin to be clinically effective in humans. In addition, insulin in some invertebrates is quite similar to human insulin, and has similar physiological effects. Although insulins from certain species are very similar, when it comes to crystal structure and amino acid sequence, the small differences can be of great importance depending on where they are located. Mercodia knows the importance of having assays optimized for various species. In addition to our human insulin assays, we offer insulin assays specifically calibrated for rodent (rat/mouse) and large animals (cat/dog/horse/pig/cow/sheep) samples. A variety of our insulin assays have also been validated (in-house or by clients) for use with other animals (monkeys/rabbit/hamster). See our Insulin ELISA C-peptide Unlike insulin, C-peptide differs greatly between species. For example, the amino acid sequence of rat C-peptide is approximately 30% different than that of primates. The structural differences across species and increased in vivo stability make C-peptide measurements more suitable (than insulin) when performing studies of xenotransplantation or subjects treated with exogenous insulin. See our C-peptide ELISA Childhood obesity Childhood obesity: a concerning epidemic Obesity is a major risk factor for many chronic diseases, such as diabetes, cancer and cardiovascular disease. Unfortunately, the prevalence of obesity has been increasing at alarming rates in children and associated chronic diseases that were once thought to be “adult onset” are being diagnosed more frequently in children. According to the World Health Organization (WHO), “childhood obesity is one of the most serious public health challenges of the 21st century”. Levels of OxLDL in children Identifying disease-related biomarkers in this population is important for understanding the development and progression of cardiovascular disease in these youngsters. A study by Kelly et al. demonstrated significantly higher levels of oxLDL in overweight/obese children when compared to normal weight children, using the Mercodia OxLDL ELISA, suggesting that the early signs of atherosclerosis can be detected in children as young as 6 years of age. “Our findings provide evidence that body fatness and insulin resistance are associated with higher levels of oxLDL early in life. Our group and others are continuing to examine the potential value of oxLDL as a way to more accurately predict which children are most likely to develop cardiovascular disease and type 2 diabetes”, says Dr. Aaron Kelly, an Assistant Professor of Pediatrics at the University of Minnesota Medical School. See our OxLDL ELISA Glucagon Glucagon in NAFLD/NASH It is well known that glucagon acts to stimulate glycogenolysis and gluconeogenesis. In addition to this, glucagon also regulates amino acid metabolism and ureagenesis and has an effect on hepatic fat metabolism. The different roles of glucagon in the metabolism makes the hepatic glucagon resistance, found in NAFLD and NASH patients, a highly interesting and important player to study. Hepatic glucagon resistance has been associated with an increase in liver lipid infiltration, and moreover NAFLD/NASH patients have elevated glucagon levels independently of diabetes. In preclinical studies it has been shown that by reducing the glucagon receptor signaling, the lipid content of the liver is increased. Moreover, the disturbed hepatic glucagon signaling in the liver leads to an increase in plasma amino acids, which subsequently give rise to hypersecretion of glucagon and hyperplasia of alpha-cells in an liver – pancreas feedback loop. No pharmacological therapies are so far approved for NAFLD treatment, however GLP-1R/glucagon receptor dual agonists are currently under investigation. As stated by Wewer Albrechtsen and colleagues in 2018- “Observations of elevated fasting concentrations of plasma glucagon levels in humans should therefore direct attention to the functional status of the liver”. References 1. Charbonneau, A., Couturier, K., Gauthier, M.-S. & Lavoie, J.-M. Evidence of Hepatic Glucagon Resistance Associated with Hepatic Steatosis: Reversal Effect of Training. Int. J. Sports Med. 26, 432–441 (2005). 2. Junker, A. E., Gluud, L., Holst, J. J., Knop, F. K. & Vilsbøll, T. Diabetic and nondiabetic patients with nonalcoholic fatty liver disease have an impaired incretin effect and fasting hyperglucagonaemia. J. Intern. Med. 279, 485–93 (2016). 3. Ali, S. & Drucker, D. J. Benefits and limitations of reducing glucagon action for the treatment of type 2 diabetes. Am. J. Physiol. Metab. 296, E415–E421 (2009). 4. Solloway, M. J. et al. Glucagon Couples Hepatic Amino Acid Catabolism to mTOR-Dependent Regulation of α-Cell Mass. Cell Rep. 12, 495–510 (2015). 5. Holst, J. J., Wewer Albrechtsen, N. J., Pedersen, J. & Knop, F. K. Glucagon and Amino Acids Are Linked in a Mutual Feedback Cycle: The Liver-α-Cell Axis. Diabetes 66, 235–240 (2017). 6. Wewer Albrechtsen, N. J. Glucagon receptor signaling in metabolic diseases. Peptides 100, 42–47 (2018). 7. Seghieri, M. et al. Future Perspectives on GLP-1 Receptor Agonists and GLP-1/glucagon Receptor Co-agonists in the Treatment of NAFLD. Front. Endocrinol. (Lausanne). 9, 649 (2018). 8. Wewer Albrechtsen, N. J. et al. Evidence of a liver–alpha cell axis in humans: hepatic insulin resistance attenuates relationship between fasting plasma glucagon and glucagonotropic amino acids. Diabetologia 61, 671–680 (2018). See our Glucagon ELISA Incretins Incretins in NAFLD/NASH Reduced secretion of GLP-1 has been found in non-diabetic NAFLD and NASH patients with impaired insulin release and a reduced incretin effect has been suggested to be an early sign of impaired glucose tolerance, associated with IR and obesity. Incretin-based therapy has proven to have an effect on liver enzymes like ALT, reduces the hepatic lipid content and improves other hepatic functional markers. Liraglutide does not interfere in the Mercodia Total GLP-1 NL-ELISA which means that the ELISA assay is suitable for GLP-1 measurements even in samples from patients undergoing analogue treatment. References 1. Bernsmeier, C. et al. Glucose-induced glucagon-like Peptide 1 secretion is deficient in patients with non-alcoholic fatty liver disease. PLoS One 9, e87488 (2014). 2. Junker, A. E., Gluud, L., Holst, J. J., Knop, F. K. & Vilsbøll, T. Diabetic and nondiabetic patients with nonalcoholic fatty liver disease have an impaired incretin effect and fasting hyperglucagonaemia. J. Intern. Med. 279, 485–93 (2016). 3. Carbone, L. J., Angus, P. W. & Yeomans, N. D. Incretin-based therapies for the treatment of non-alcoholic fatty liver disease: A systematic review and meta-analysis. J. Gastroenterol. Hepatol. 31, 23–31 (2016). See our Total GLP-1 NL-ELISA Insulin Insulin in NAFLD/NASH Insulin resistance (IR) is highly associated with NAFLD/NASH and NAFLD is present in up to 2/3 of patients with T2DM. Also in lean patients with normal glucose tolerance there is an association between IR and NAFLD, which in this case could represent an initial stage of MetS. There are also genetic variants that is strongly associated with NAFLD/NASH, but is not characterized by features of the metabolic syndrome, like the IR. One example of this is the polymorphism in PNPLA leading to the missense mutation I148M giving a susceptibility to NAFLD. Hepatic insulin resistance leads to an increase in glucose production and hyperglycemia and the hepatic lipid and lipoprotein metabolism is also severely affected. In NAFLD, insulin resistance contributes to increased hepatic fatty acids (FA) via de novo lipogenesis (DNL) and via increased lipolysis in white adipose tissue. Hepatic accumulation of lipids reduces hepatic responsiveness to insulin, resulting in an increase of glucose in circulation and thus of insulin, producing a state of chronic hyperinsulinemia. Whether IR triggers lipid accumulation in the liver, or whether fat deposition in the liver alters the hepatic response to insulin, is not clear. In obese and diabetic patients, peripheral IR may be initially responsible for the accumulation of fat in the liver, whilst diet can be the primary cause of hepatic steatosis and hepatic insulin resistance in non-obese patients. Immune cells, such as macrophages, contributes to the progression of NASH and dysregulation of macrophages are closely related to multiple metabolic disorders and among them NAFLD. Excessive hepatic lipid accumulation promotes the activation of macrophages/Kupffer cells to exacerbate IR, as well as hepatic inflammation and fibrogenesis. HOMA-IR has recently been suggested to be a diagnostic tool for NAFLD, since the upper limit of HOMA-IR in population-based cohorts closely corresponds to that of normal liver fat. Multiple organs are involved in the development and progression of NAFLD and NASH and the IR plays a central role in all tissues. References 1. Khan, R., Bril, F., Cusi, K. & Newsome, P. N. Modulation of Insulin Resistance in NAFLD. Hepatology hep.30429 (2018). doi:10.1002/hep.30429 2. Marchesini, G. et al. Association of nonalcoholic fatty liver disease with insulin resistance. Am. J. Med. 107, 450–5 (1999). 3. Romeo, S. et al. Genetic variation in PNPLA3 confers susceptibility to nonalcoholic fatty liver disease. Nat. Genet. 40, 1461–1465 (2008). 4. Reccia, I. et al. Non-alcoholic fatty liver disease: A sign of systemic disease. Metabolism. 72, 94–108 (2017). 5. Kitade, H., Chen, G., Ni, Y. & Ota, T. Nonalcoholic Fatty Liver Disease and Insulin Resistance: New Insights and Potential New Treatments. Nutrients 9, 387 (2017). 6. Isokuortti, E. et al. Use of HOMA-IR to diagnose non-alcoholic fatty liver disease: a population-based and inter-laboratory study. Diabetologia 60, 1873–1882 (2017). See our Insulin ELISA Oxidized LDL Oxidized LDL in NAFLD/NASH Oxidative stress is an important pathological event in the progression of NAFLD towards NASH, and levels of OxLDL has been shown to be increased in NASH compared with controls. The role of OxLDL is far from fully understood, but several studies have demonstrated a relationship between NAFLD/NASH and oxLDL. In 2008 it was shown that a postprandial increase in oxLDL independently predicted the severity of liver histology in subjects with NAFLD. The authors suggested that oxLDL could be a biological mechanism for the epidemiological association between liver disease and atherosclerosis in NASH. In the liver, oxLDL gets trapped inside lysosomes of Kupfer cells, which is liver specific macrophages, leading to increased hepatic inflammation and inflammatory gene expression. In a high fat diet (HFD) mouse model, oxLDL administration aggravated the hepatic steatosis, the fibrosis, and also resulted in intense inflammation and severe hepatic injury, which are the typical histological features of NASH. These results suggest that an increased oxidative state, including HFD-induced intracellular lipid peroxidation and its extracellular source from oxLDL, is the actual trigger for hepatic inflammation in which liver injury is mediated. The involvement of oxLDL in NASH indeed needs further investigation according to a Dutch research group that states “Despite the relationship between oxLDL and liver steatosis, further studies are clearly needed to assess the clinical adequacy of plasma oxLDL as a noninvasive biomarker for NASH and to provide a basis for therapeutic strategies to improve NASH and related metabolic risk factors that conduct to T2D, cardiovascular diseases, and liver deterioration”. References 1. Walenbergh, S. M. A., Koek, G. H., Bieghs, V. & Shiri-Sverdlov, R. Non-alcoholic steatohepatitis: The role of oxidized low-density lipoproteins. J. Hepatol. 58, 801–810 (2013). 2. Spahis, S., Delvin, E., Borys, J.-M. & Levy, E. Oxidative Stress as a Critical Factor in Nonalcoholic Fatty Liver Disease Pathogenesis. Antioxid. Redox Signal. 26, 519–541 (2017). 3. Chalasani, N., Deeg, M. A. & Crabb, D. W. Systemic levels of lipid peroxidation and its metabolic and dietary correlates in patients with nonalcoholic steatohepatitis. Am J Gastroenterol 99, 1497–1502 (2004). 4. Musso, G. et al. Association of liver disease with postprandial large intestinal triglyceride-rich lipoprotein accumulation and pro/antioxidant imbalance in normolipidemic non-alcoholic steatohepatitis. Ann. Med. 40, 383–394 (2008). 5. Bieghs, V. et al. Trapping of oxidized LDL in lysosomes of Kupffer cells is a trigger for hepatic inflammation. Liver Int. 33, 1056–61 (2013). 6. Yimin et al. A novel murine model for non-alcoholic steatohepatitis developed by combination of a high-fat diet and oxidized low-density lipoprotein. Lab. Invest. 92, 265–81 (2012). See our OxLDL ELISA Biomarkers for risk prediction Reliable biomarkers of a developing atherosclerosis are of great importance. Serum level of low-density lipoprotein (LDL) cholesterol is an important factor in the development of atherosclerosis, and still remains the primary target of therapy for the prevention of coronary heart disease (CHD). However, elevated serum level of LDL-cholesterol, even together with other risk factors, is a poor predictor of a future CHD event. CHD risk functions like Framingham (score includes gender, age, total cholesterol, HDL-cholesterol, cigarette smoking, blood pressure and diabetes) and PROCAM (score includes gender, age, cigarette smoking, blood pressure, history of diabetes, blood levels of LDL-cholesterol, HDL-cholesterol, sugar and triglycerides and heart attack history) seem to over or under estimate the risk of future CHD events in several populations. Sharper tools are indeed needed for risk assessment of CHD and biomarkers like myeloperoxidase (MPO), lipoprotein(a) (Lp(a)) and oxidized low-density lipoprotein (oxLDL) may prove to be useful complements to currently used risk scores. Myeloperoxidase (MPO) There is strong evidence that circulating markers of inflammation are closely associated with the development of subsequent cardiovascular events. MPO, an enzyme released from activated neutrophiles and monocytes as a result of inflammation, has in several studies been used as a marker for coronary artery disease (CAD). In a study by Ndrepepa and coworkers at the German Heart Center in Munich, Germany, MPO levels were not only found to be elevated in CAD patients, but also to be increased with the severity of CAD. Since MPO was also shown to have a stronger correlation to acute coronary syndromes (ACS) than CRP, the authors hypothesize that MPO may have a direct involvement in plaque destabilization. Baldus and colleagues at the University of Hamburg, Germany support this hypothesis and suggest that MPO might serve as both a marker and mediator of vascular inflammation in patients with acute coronary syndrome (ACS). Try our MPO ELISA Oxidized LDL LDL refers to a class of lipoproteins which main function is to transport cholesterol and triglycerides in the blood for use by various cells. Due to the high blood pressure, plasma constituents continuously seep into the intima of arteries and, at reasonable blood levels LDL particles can pass in and out of the vessel wall. In the blood, LDL particles may be protected from oxidation by blood antioxidants. In excess, LDL tends to get trapped in the matrix, by proteoglycans and other extracellular matrix constituents, where it is subjected to modifications. It has been suggested for more than 20 years that oxidation of lipoproteins is central in the initiation and progression of atherosclerosis, from the early stage conversion of monocytes/macrophages into lipid-laden foam cells and fatty streaks to the late-stage development of coronary artery stenosis, plaque instability, plaque rupture, coronary thrombosis and MI. The oxidative modification hypothesis is based on the concept that LDL in its native form is not atherogenic, and that oxidation of LDL lipids and ApoB-100 is central in the pathogenesis of vascular disease. Whereas native (unmodified) LDL lacks inflammatory properties, modified LDL particles are recognized by the body as foreign, which in turn triggers activation of the immune system and initiation of inflammation. Try our OxLDL ELISA Lipoprotein (a) Multiple risk factors usually contribute to the development of atherosclerosis. Hence, family history of premature or aggressive CVD is an important consideration. For example, circulating levels of Lp(a) are inherited and elevated levels have shown to be an independent risk marker for MI. Blood levels of Lp(a) are determined by polymorphism of the LPA gene, coding for the Apo(a) moiety of the Lp(a) particle. It has been shown that Apo(a) size, which depends of the number of kringle IV–type 2 (KIV-2) repeats, inversely correlates with circulating Lp(a) levels. High levels of Lp(a) have been associated with heart disease since 1970s, but it has been difficult to detect whether Lp(a) actually plays a causative role. Recent findings, however, have provided support for a causal association of Lp(a) with coronary disease. In a publication, Kamstrup and coworkers at the Herlev Hospital, Denmark reported their observed associations of elevated levels of Lp(a) as well as the genetic variation raising the Lp(a) levels with MI risk. These findings were supported by the work of Clarke and colleagues at the University of Oxford, United Kingdom, who identified two LPA variants that were found to be strongly associated with increased Lp(a) levels, a reduced number of KIV-2 repeats, a small Lp(a) size and an increased risk for coronary disease. In an editorial to Kamstrup et al. Thanassoulis and O’Donnell, both affiliated to the National Heart, Lung, and Blood Institute’s Framingham Heart Study, Framingham, MA, USA, correspond that “this is an important biological finding that elevates the status of Lp(a)”. The Apo(a) moiety of the Lp(a) particle shares sequence homology with plasminogen, which in circulation is converted to its active form plasmin, an important enzyme that inhibits thrombus formation through fibrinolysis. Recent studies have shown that the plasminogen-like Apo(a) competes with plasminogen for binding to the plasminogen receipt and thus inhibits plasminogen activation and consequently the formation of plasmin. These properties of Apo(a) may explain the association of high Lp(a) concentrations with MI. Try our Lp(a) ELISA Measurement of Oxidized LDL Given the suggested patho-physiological significance of oxLDL, there has been a significant increase in the research of the pathogenic role of circulating oxLDL, giving new insights and questions in regards to the role of oxLDL in cardiovascular disease. Previously, indirect methods were used to assess LDL oxidation, mainly by measurement of susceptibility of LDL to oxidation and measurement of auto-antibodies to oxLDL. The introduction of specific monoclonal antibodies, which recognize different distinct oxidation epitopes has enabled the development of specific and sensitive immunoassays to measure the levels of circulating oxLDL. Evidence on circulating oxLDL and its clinical significance to the diagnosis and prognosis of cardiovascular disease has grown substantially over the past years. Three different monoclonal antibodies that bind various epitopes of oxLDL have been described in detail. All three are murine monoclonal antibodies (DLH3, E06 and 4E6) and assays based on these antibodies have been used in various investigations. The monoclonal antibody 4E6 is directed against a conformational epitope in the ApoB-100 moiety of the LDL particle, generated as a consequence of substitution of at least 60 lysine residues of ApoB-100 with aldehydes. This number of substituted lysines also corresponds to the minimal number required for scavenger-mediated uptake of oxLDL. The specificity of the antibody 4E6 is different from the specificities of DLH3 and E06, which are reported to be directed against epitopes of oxidized phosphatidylcholine respectively to the phosphocholine head group, of oxidized but not native phospholipids. Try our OxLDL ELISA Porcine Porcine models Porcine models have been used in research for decades due to their anatomical and physiological similarities to humans. Furthermore, pigs are, like humans, omnivorous. For these reasons, pigs have been broadly used in research areas such as diabetes, obesity and islet transplantation (Minali-Nejad et al. 2014; Vodicka et al. 2005). Islet cell transplantation has long been considered a potential cure for type 1 diabetes. The shortage of human donors and difficulty in isolating purified islets from adult human pancreata has drawn attention to the use of porcine islets. Pig and human insulin are structurally similar, and the regulation of insulin secretion in pigs resembles that of humans. Thus, pigs have not only served as an important translational model for diabetes but porcine islets are also used in xenotransplantation studies. To investigate the viability of the transplanted islets, parameters such as insulin and c-peptide are measured (Bogdani et al. 2005; Dufrane et al. 2006; Groth et al. 2000; Klymiuk et al. 2012; Korsgren et al. 1991). Moreover, the Mercodia Porcine C-peptide ELISA is an assay optimized specifically for porcine C-peptide without cross-reactivity to proinsulin or insulin. There is also no cross-reactivity to C- peptide in human, macaque, rat or mouse samples, which makes it possible to distinguish porcine C-peptide produced by transplanted islets from exogenously administered insulin in serum or plasma samples. References 1. Bogdani M et al. (2005) Growth and Functional Maturation of beta-cells in Implants of Endocrine Cells Purified From Prenatal Porcine Pancreas. Diabetes 54:3387-3394. 2. Dufrane D et al. (2006) Parameters favouring successful adult pig islet isolations for xenotransplantation in pig-to-primate models. Xenotransplantation 13:204-214. 3. Groth CG et al. (2000) Clinical aspects and perspectives in islet xenotransplantation. J Hepat Pancreat Surg 7:364-369. 4. Klymiuk N et al. (2012) Xenografted Islet Cell Clusters From INSLEA29Y Transgenic Pigs Rescue Diabetes and Prevent Immune Rejection in Humanized Mice. Diabetes 61:1527-32. 5. Korsgren O et al. (1991) Functional and morphological differentiation of fetal porcine islet-like cell clusters after transplantation into nude mice. Diabetologia 34:379-386. 6. Milani-Nejad N et al. (2014) Small and large animal models in cardiac contraction research: Advantages and disadvantages. Pharmacol Ther 141:235-49. 7. Ramsay TG et al. (2004) Peripheral leptin administration alters hormone and metabolite levels in the young pig. Comp Biochem Physiol A Mol Integr Physiol. 138:17-25. Mercodia Porcine Insulin ELISA Mercodia Porcine C-peptide ELISA Non human primates Merocodia assays in NHP research Mercodia assays have been used with NHP samples for many years. Below is a list of some of these groups, as well as the Mercodia assays used in the highlighted publications. Tissue-specific regulation and expression of heat shock proteins in type 2 diabetic monkeys Wake Forest University – Proinsulin, Insulin, C-Peptide & OxLDL Proof of mechanism and PK and PD relationship study in cynomolgus monkeys Pfizer, Covance & University of Birmingham – Insulin In vivo Imaging of Autologous Islet Grafts in the Liver and Under the Kidney Capsule in Non-Human Primates Harvard, Mass. Gen. Hospital & Joslin Diabetes Center – Insulin Effect of a PPARa agonist on plasmalipids, lipoproteins, and glycemic control in diabetic monkeys Wake Forest University & Pfizer – Insulin & C-Peptide Canine Health and Nutrition Dogs are susceptible to several diseases that also occur in humans, including diabetes, dental and heart disease, epilepsy, cancer, hypothyroidism, and arthritis. Dogs often share their environment with their owner. If there is something in the environment that triggers a disease in humans, it is possible that this will also affect the dog. Dog diabetes Like humans, dogs can also develop diabetes. Diabetes mellitus (DM) is a life-threatening disease that is diagnosed by high blood glucose. Canine diabetes is further divided into two groups, Type 1 DM (insulin deficiency diabetes) or Type 2 DM (insulin resistance diabetes). These types are similar to those that affect humans. Type I diabetes occurs during the early years of the dog’s life and is also referred to as juvenile dog diabetes. Type II diabetes is normally observed in senior dogs and typically occurs between 5 and 12 years of age. Diabetes typically occurs in dogs that have passed its middle age and is less common in dogs under 3 years of age. Aside from older dogs, some breeds are more susceptible to dog diabetes than others. Obese female dogs are also more prone to diabetes. Diabetes is considered an autoimmune disease and is hereditary in dogs. It may lead to further system malfunctions and early diagnosis is crucial so that the dog’s disease can be reversed. Hyperglycemia is a major initiator of a range of circulatory complications and diabetes can lead to a variety of secondary diseases, especially heart disease. Management of canine diabetes starts with a proper diet. There are foods that the dog should eat and other to avoid in order to helping this condition. Certain medications may be required for dogs suffering from the worst forms of this disease. Insulin injections are important and can extend the dog’s life and allow it to live normally despite having diabetes. References Catchpole et al. (2005) Canine diabetes mellitus: can old dogs teach us new tricks? Diabetologia 48:1948-1956. Fall (2009) Characterisation of Diabetes Mellitus in Dogs. Acta Universitatis agriculturae Sueciae, 1652-6880; 2009:45 ISBN 978-91-86195-92-2. Diet In that the dogs’ medical care and food quality increases, the dogs tend to live longer. Hence, identification of physiological changes that change the dogs’ nutritional needs during its lifetime is of interest. Although reference levels for serum metabolites and blood values have been established for healthy adult dogs, these guidelines may not be appropriate for evaluating the health of weanling or aged animals. Body size is a factor in the classification of the dog’s life stage (large breeds are considered to be older at a younger chronological age than smaller breeds), but most considered “old” when they reach 7 to 10 years of age. By identifying changes in levels of serum metabolites, hematology values, and digestive efficiency in older dogs, the researchers can formulate diets that are more suited for this life stage. Conversely, the juvenile dogs also have unique physiological characteristics and nutritional needs. References Kuzmuk et al. (2005) Diet and Age Affect Intestinal Morphology and Large Bowel Fermentative End-Product Concentrations in Senior and Young Adult Dogs. J Nutr 135:1940-1945. Swanson et al. (2004) Diet affects nutrient digestibility, hematology, and serum chemistry of senior and weanling dogs. J Anim Sci 82:1713-1724. Research model Most of the basic and preclinical research is performed on rodents. However, inclusion of a non-rodent species is required in safety assessment of pharmaceuticals. The use of a non-rodent species for the characterization of new drugs is intended to limit the uncertainty in the extrapolation process from animal toxicity data to the human situation. Such uncertainties are species variation, scaling from small, short-lived animals to larger, long- lived species, and the use of a homogeneous animal population. The dog is an important respiratory and cardiovascular research model. Beagle is the dog breed most often used in animal testing due to their size and gentle nature and they are commonly used as models for human diseases in cardiology and endocrinology. References SCHER, (2009) Scientific Committee on Health and Environmental Risks: The need for non-human primates in biomedical research, production and testing of products and devices (Opinion report). NCB, (2005) Nuffield Council On Bioethics: The ethics of research involving animals (Report). Mercodia Canine Insulin ELISA The Mercodia Canine Insulin ELISA constitutes a link between pre-clinical research and clinical phase trials and is also commonly used by those conducting veterinary research. This is an immunoassay for the quantitative determination of canine insulin in serum or plasma and specially optimized for canine samples. The Mercodia Canine Insulin ELISA Reference values Samples were collected from 34 dogs of 18 different breeds aged 2–10 years old. Insulin was measured with the Mercodia Canine Insulin ELISA (10-1203-01). The mean insulin concentration in the 34 clinically healthy fasted dogs was 165 ng/L (median 154 ng/L, range 40–385 ng/L). References Provo et al. (2008) A novel sandwich ELISA for the measurements of insulin in canine serum and plasma. Poster presented at AAVLD, Reno, USA. Öberg et al. (2011) Validation of a species-optimized enzyme-linked immunosorbent assay for determination of serum concentrations of insulin in dogs. Vet Clin Pathol 40: 66–73 Feline Diabetes Mellitus is one of the most frequently diagnosed endocrinopapathies in cats. Mercodia Feline Insulin ELISA Equine Equine metabolic syndromes The term “equine metabolic syndrome” describes a collection of clinical signs that contribute to the development of founder (laminitis) in horses. This syndrome is currently defined by the presence of 1) insulin resistance (IR), 2) obesity and/or regional adiposity, and 3) prior or current laminitis. IR is characterized by a loss of insulin sensitivity at the cellular level. The insulin resistant horse is carbohydrate intolerant and the degree of IR can be assessed and measured as part of the diagnosis and recovery process (Frank, 2011; Ralston, 2002). In the majority of cases, horses compensate for IR by increasing insulin secretion from the pancreas. There is a need to accurately diagnose IR in clinical practice. However, studies have shown that diagnosis of IR should not be made solely based on results from the glucose curve of the CGIT (combined glucose-insulin tolerance test). Therefore, insulin dynamics are of great importance when diagnosing a horse with IR (Bröjer et al. 2013). Matching energy intake with energy expenditure is important for the exercising horse. Parameters such as the hormones ghrelin, adiponectin and insulin have been shown to play a major role in mediating the energy balance either through their effects on feed intake or metabolic regulation. Glucose and insulin responses are modified by multiple factors including diet, obesity, exercise level and stress. (Jansson, 2002; Gordon, 2006; Nostell, 2012; Ralston, 2002). References 1. Jansson A et al. (2002) Effects on exercise metabolism of varying starch and sugar proportions. Equine Vet J Suppl 34:17-21. 2. Bröjer J et al. (2013) Repeatability of the Combined Glucose-Insulin Tolerance Test and the Effect of a Stressor before Testing in Horses of 2 Breeds. J Vet Intern Med In press. 3. Frank N (2011) Equine Metabolic Syndrome. Vet Clin North Am Equine Pract. 27:73-92. 4. Gordon ME et al. (2006) Exercise-induced alterations in plasma concentrations of ghrelin, adiponectin, glucose, insulin, and cortisol in horses. Vet J 173:532-540. 5. Nostell K et al. (2012) Repeated post-exercise administration with a mixture of leucine and glucose alters the plasma amino acid profile in Standardbred trotters. Acta Vet Scand 1:54:7. 6. Ralston SL (2002) Insulin and glucose regulation. Vet Clin North Am Equine Pract 18:295-304. Mercodia Equine Insulin ELISA Bovine Dairy cow Dairy cows are bred for the ability to produce large yields of milk, from which dairy products are made. To be able to maintain high milk production, a dairy cow must be bred and produce calves. High-producing dairy cows require large amounts of high-energy feeds to meet nutritional requirements for maintenance, milk synthesis and reproduction.(1) Therefore, there are many nutrition studies involving dairy cows.(2-5) High-starch–low-fiber diets, through shifting the pattern of end products of ruminal fermentation towards greater propionate and lactate, provide readily available sources of energy (i.e. glucose) for growing muscles. This will often lead to greater feed conversion efficiency and growth.(2) The changed nutrient partitioning involves decreased lipogenesis and increased lipolysis, and results in a state of negative energy balance (NEB). Although mobilization of body fat tissue is a physiological adaptation in the high-yielding dairy cow in NEB, this can have a negative effect on health and productivity.(6) In early lactation, cows with metabolic profiles indicating a severe NEB, such as high concentrations of NEFA, have an increased risk of diseases such as clinical ketosis, displaced abomasum, metritis, or retained placenta.(7)Furthermore, insulin is an important regulator of lipogenesis. It is shown that dairy cows selected for increased milk yield have greater insulin resistance, which is associated with increased body lipid mobilization and lower body condition score.(1, 6) Plasma concentrations of insulin generally decline postcalving and this decline is often associated with a more severe NEB, which makes it an important biomarker to understand the health of the animal.(8) Heat stressed cows In warmer parts of the world reduced milk production results from a phenomenon called heat stress. It counteracts the genetic progress achieved in increasing milk production in cows.(9) Heat stress has an enormous economic impact on the global dairy industry. Factors contributing to this economic issue include decreased milk production, increased metabolic disorders and health problems, slow heifer growth, compromised milk quality, and reduced reproductive performance.(9) Lactating cows prefer ambient temperatures ranging between 5-25°C. At temperatures above that, the cow can no longer cool herself adequately and thereby enters heat stress.(7) Elevated respiration rates and rectal temperatures, impaired metabolism and poor reproductive performance characterize heat stress in cows.(7) Furthermore, high-producing cows have been found to be more affected by heat stress than its low-producing counterparts due to that the zone of thermal neutrality shifts to lower temperatures as milk production, feed intake and metabolic heat production increase.(7) The decreased milk yield caused by heat stress is partly caused by reduced nutrient intake and partly by an energy-intake independent change in nutrient partitioning, such as the metabolic profile.(10) In cattle, the heat stress increase insulin secretion and decrease insulin sensitivity, making insulin an important biomarker to measure insulin resistance.(10) References Ingvartsen KL and Andersen JB (2000) Integration of metabolism and intake regulation: a review focusing on periparturient animals. J Dairy Sci 83(7):1573-1597. Graugnard DE, Berger LL, Faulkner DB and Loor JJ (2010) High-starch diets induce precocious adipogenic gene network up-regulation in longissimus lumborum of early-weaned Angus cattle. Br J Nutr 103(7):953-963. Mullins CR, Mamedova LK, Brouk MJ, Moore CE, Green HB, Perfield KL, Smith JF, Harner JP and Bradford BJ (2012) Effects of monensin on metabolic parameters, feeding behavior, and productivity of transition dairy cows. J Dairy Sci 95(3):1323-1336. Blanch M, Calsamiglia S, Devant M and Bach A (2010) Effects of acarbose on ruminal fermentation, blood metabolites and microbial profile involved in ruminal acidosis in lactating cows fed a high-carbohydrate ration. J Dairy Res 77(1):123-128. Odensten MO, Berglund B, Persson Waller K and Holtenius K (2007) Metabolism and udder health at dry-off in cows of different breeds and production levels. J Dairy Sci 90(3):1417-1428. Roche JR, Friggens NC, Kay JK, Fisher MW, Stafford KJ and Berry DP (2009) Invited review: Body condition score and its association with dairy cow productivity, health, and welfare. J Dairy Sci 92(12):5769-5801. Kadzere CT, Murphy MR, Silanikove N and Maltz E (2002) Heat stress in lactating dairy cows: a review. Livestock Production Science 77:59-91. Beam SW and Butler WR (1999) Effects of energy balance on follicular development and first ovulation in postpartum dairy cows. J Reprod Fertil Suppl 54:411-424. Collier RJ, Dahl GE and VanBaale MJ (2006) Major advances associated with environmental effects on dairy cattle. J Dairy Sci 89(4):1244-1253. Wheelock JB, Rhoads RP, Vanbaale MJ, Sanders SR and Baumgard LH (2010) Effects of heat stress on energetic metabolism in lactating Holstein cows. J Dairy Sci 93(2):644-655. Mercodia Bovine Insulin ELISA Ovine A decrease in dry matter intake is a major physiological change in ruminants. Mercodia Ovine Insulin ELISA GLP-1 GLP-1 White paper Important aspects when measuring to consider when measuring GLP-1. You will find answers to: Why measure total GLP-1? Why is amidated GLP-1 important? What is the expected levels of GLP-1 in different samples? How is GLP-1 targeted in treatment strategies? Download White paper Glucagon Glucagon White papers Glucagon measurement: Adressing long-standing analytical challenges Glucagon is a fundamental regulator of glycemic control, through its ability to stimulate hepatic glucose production, and numerous studies have demonstrated that there is dysregulation of glucagon in all forms of diabetes. Interest in this pancreatic alpha cell hormone has grown considerably in recent years and with advances in the drug development arena, the need to address long-standing analytical obstacles has become even more urgent. Download White paper Glucagon: metabolic regulator critical for pathophysiology and pharmacology Why don’t changes in insulin fully explain changes in glycemic control? How is glucagon affected by therapies currently approved or under investigation? During studies in which a metabolic marker(s) is included, why consider glucagon? Get answers to these questions and understand why Glucagon is an important biomarker for diabetes research. Download White paper Even more specific glucagon measurements How the introduction of a specific glicentin ELISA made glucagon measurements even more specific – a fruitful collaboration with academic researchers. Download White Paper Switching from RIA to ELISA Switching from RIA to ELISA RIAs and ELISAs are methods used extensively to detect and quantify biomarkers. Assoc. Prof. Dr. Rodrig Marculescu is Head of Endocrinology at the Department of Laboratory Medicine, Medical University of Vienna, Austria, where they perform about one million hormone analyses per year, including about 500 glucagon analyses. His lab has changed from Glucagon RIA to the Mercodia Glucagon ELISA. Read about his experiences and why he thinks the Mercodia Glucagon ELISA is superior to other assays. Download Case study Science talks Mercodia webinars connect you with experts in various scientific fields. These special sessions are educational, informative and often provide you with answers to questions that may assist your group in making decisions now or in the future. All of our webinars are recorded and posted below as an added resource for our valued customers. Find all Mercodia Webinars here! The Gut Microbiota in Diabetes The Gut Microbiota in Diabetes Welcome to a new Mercodia Webinar where Professor Fredrik Bäckhed from Gothenburg University will discuss the influence of the gut microbiota in type 2 diabetes. During the webinar, Professor Bäckhed will discuss the influence of the gut microbiota in type 2 diabetes. The microbial ecosystem, microbiota, of the human gut consists of trillions of bacteria and recent data have demonstrated that an altered gut microbiota can be associated with a number of diseases, ranging from obesity and inflammatory diseases to behavioral abnormalities. Here, a study where the microbiota of individuals with prediabetes and screen-detected type 2 diabetes was assessed is presented. There is extensive microbiota-host cross talk that generates signals to extraintestinal organs and it is becoming more evident that a miss-configured microbiota may produce signals that contribute to metabolic diseases. Exploring how microbially produced metabolites contribute to disease may provide foundations for novel treatment strategies based on the microbiota. Professor Fredrik Bäckhed combines clinical oriented research with gnotobiotic mouse models to address the role of the normal gut microbiota in cardiometabolic diseases. He holds a PhD from Karolinska Institute, Sweden in 2002 and performed his postdoctoral training at Washington University, St Louis where he identified the gut microbiota as an environmental factor that regulates adiposity and obesity. Fredrik is a professor at the University of Gothenburg and is also appointed Professor at the University of Copenhagen. He has been guest Professor at University of Oslo and recently did a sabbatical at University of Hawaii. Watch the webinar Alpha-cells and the incretin effect The alpha-cell is critical for the full incretin effect Classically, glucagon is viewed as a counter-regulatory hormone that opposes insulin action. Yet, glucagon stimulates insulin secretion in a glucose-dependent manner, suggesting a more complex role for this hormone in glucose homeostasis. Glucose-dependent insulinotropic polypeptide (GIP) is an incretin that potentiates insulin secretion at high glucose and glucagon secretion in the presence of amino acids, indicating these cells have preferential nutrient settings. Moreover, the receptor for GIP is expressed in a-cells and is involved in glucagon secretion. In this webinar, Drs Megan Capozzi and Kimberley El will discuss their recent work on alpha-cell to beta-cell communication through proglucagon products and how this system is utilized by GIP to control insulin secretion and postprandial glucose homeostasis. Drs. Megan Capozzi and Kimberley El are postdoctoral researchers at Duke University at the Duke Molecular Physiology Institute. Megan received her Ph.D. from Vanderbilt University in 2016 and Kimberley received her Ph.D. from Indiana University School of Medicine in 2018. Megan and Kim work in the lab of Dr. Jonathan Campbell, where they focus on the role of the alpha-cell in postprandial glucose metabolism. Watch the webinar! Glucagon and Liver Disease Hyperglucagonemia - A disrupted liver-alpha cell crosstalk in fatty liver disease In this webinar, we are proud to introduce Dr. Nicolai J. Wewer Albrechtsen as our speaker. Dr. Wewer Albrechtsen earned his MD at the University of Copenhagen in 2014. Since 2011, he has been working on gastrointestinal hormones and in particular glucagon biology. In recent years, Dr. Wewer Albrechtsen has initiated his own scientific group that investigates the actions of glucagon in dysmetabolic conditions such as diabetes, obesity, and NAFLD with the use of state-of-art technologies including mass spectrometry. The group currently focuses on exploring a new physiological term known as glucagon resistance, identified in mice and humans with NAFLD. During the webinar, Dr. Wewer Albrechtsen discusses current data for how hyperglucagonemia may or may not mark a disrupted liver-alpha cell axis. Watch the webinar Glucagon Signaling Glucagon Signaling in Obesity and Type 2 Diabetes Welcome to this webinar on glucagon signaling in obesity and type 2 diabetes with Dr Jennifer Stern from the University of Arizona. During the webinar, Dr Stern talks about the metabolic actions of glucagon and why blockage of the glucagon receptor is beneficial for lowering glucose levels. Moreover, discusess the glucagon:insulin ratio and what additional information it gives in T2DM, compared with measuring the two hormones individually. Watch the webinar Context of Use How can you ensure that your bioanalytical method covers your study needs? Biomarkers and PK-studies have powerful roles to play in the drug development process. They have the potential to improve success rate and reduce uncertainty in regulatory decisions at several stages of a project. How should you tailor your bioanalytical strategy to fit your study needs at all project stages? Are you sure you have all aspects covered? Can you ensure a successful method validation outcome even before you start? Watch the webinar Treatment Options for Obesity and Diabetes Novel Polypharmacological Treatment Options for Obesity and Diabetes Dr. Timo Müller, Director (acting), Institute for Diabetes and Obesity, Helmholtz Center, Munich, Germany presents an introduction to the physiology and pathophysiology of GIP and GLP-1 in metabolic diseases and treatment strategies based on (poly-)agonists of incretins and related hormones. Watch the webinar Insulin and Glucagon Regulation Insulin and Glucagon Regulation in Health & Disease Insulin and glucagon work in concert to modulate blood glucose levels. The release of these hormones, from pancreatic islets, is a highly regulated process that involves complex pathways. During this webinar, Professor Patrik Rorsman will present data on precise mechanisms regulating the secretion of these two hormones under normal conditions. He will also discuss how these pathways go awry during diabetes and how they may be targeted by therapeutic strategies. Watch the webinar Welcome to get in touch with us! General queries Email:info-global@mercodia.com Phone:+46 18 57 00 70 Support Email:support@mercodia.com Phone:+46 18 57 00 70 Place orders at Global:orders-europe@mercodia.com Phone:+46 18 57 00 71 USA:orders-usa@mercodia.com We have offices in several parts of the world and subsidiaries both in Europe and in the US. Headquarters Sylveniusgatan 8A, SE-754 50 Uppsala, Sweden Email:info-global@mercodia.com Phone:+46 18 57 00 70 Mercodia France SAS 10 rue Emile Allez 75017 Paris Email:myriam.kacimi@mercodia.com Email:info-europe@mercodia.com Phone:+ 33 6 13 38 48 02 Mercodia Inc 1590 Westbrook Plaza Drive Suite 201 Winston Salem NC 27103, United States Email:info-usa@mercodia.com Phone:(336) 725-8623 Find your local distributor here. Social media Join our social media to follow the latest updates and news! Social media LinkedIn Twitter Facebook YouTube Press Email:press@mercodia.com (c) Mercodia AB 2019 We use cookies to give you the best experience on our website. Do you agree to the use of cookies? Read more Agree