Final Report for GNE13-067
Project Information
Periparturient dairy cows lose weight due to reduced energy intake and increased energy expenditure. Overfeeding cows during late gestation results in excessive weight gain and adiposity (i.e. overconditioning) leading to the development of an “overnutrition syndrome”, similar to overweight monogastrics. Compared to lean cows, overconditioned cows exhibit greater insulin resistance and lipolysis resulting in elevated blood nonesterified fatty acid (NEFA) levels that predispose them to postpartum metabolic disease (PMD); compromising milk production, fertility, and health. Approaches to monitor pre-onset insulin resistance and hyperlipidemia represent a new means to prevent PMD, reduce economic losses, and ensure sustainability of the northeast dairy industry. Our goal was to identify blood metabolites in overconditioned dry cows that could predict pre-onset PMD. To achieve this goal we employed mass spectrometry to identify blood metabolites predicting pre-onset insulin resistance and hyperlipidemia in overweight dairy cows. Targeted predictors were ceramides, fatty acylcarnitines, and saturated fatty acids, lipid mediators and biomarkers for insulin resistance in monogastrics. Mass spectrometry was utilized to identify whether these lipid mediators predict for pre-onset PMD. Using sixty transition cows in a commercial herd, blood was collected at -45, -30, -15, -7, and +4 d relative to calving, and body condition scores (BCS) and weight were recorded. Prepartum physical activity, and PMD incidence and reproductive success were also monitored. We observed that overconditioned transition cows mobilize more NEFA earlier and exhibited a greater degree of insulin resistance when compared with lean cows. In relation to our goal, we observed a significant increase in plasma ceramides as parturition approached, an accelerated response in overconditioned transition dairy cows. Furthermore, we demonstrated that ceramides are negatively correlated with insulin sensitivity and positively correlated with NEFA mobilization. We also observed modified fatty acylcarnitine and fatty acid profiles in overconditioned transition dairy cows. Our findings demonstrate that ceramides are a new biomarker for insulin resistance and NEFA mobilization in transition dairy cows.
Manuscripts related to this work are in the process of submission to peer-reviewed journals. Further data will be available upon publication.
Introduction:
The purpose of this project is to use an innovative mass spectrometry-based approach to discover new blood metabolites that can be monitored to identify prepartum dairy cows at an enhanced risk for developing postpartum metabolic disease (PMD). The transition from gestation to lactation is characterized by an increase in energy expenditure and a decrease in dry matter intake, causing negative energy balance. This state of energy deficit increases adipose tissue lipolysis thus causing severe weight loss and increasing the circulation of nonesterified fatty acids (NEFA) in blood; hallmark characteristics of PMD (1-3). Overconditioned transition cows are at greater risk for developing PMD (2,4). Feeding a high plane of nutrition during the dry period can increase the incidence of hyperlipidemia (i.e. increased NEFA in blood, peaking during the first week postpartum), ketosis (i.e. increased β-hydroxybutyrate (BHBA) in blood), fatty liver, oxidative stress, inflammation, and compromised immune function (5-10). Ultimately PMD will reduce milk production, impair fertility, and increase culling prevalence (6,11-13). As a consequence, poor transition health can negatively impact producer profit margins. For example, ketosis treatment costs $60 million annually, and cases of ketosis result in a loss of 1.0-10 kg of milk per day per cow depending on severity, which results in an annual economic loss of $280 million nationally (10,14,15). In addition to reduced milk production, postpartum cows with a serum NEFA concentration exceeding 313 µmol/L are 20 times less likely to conceive by day 150 of lactation (16). Preventing the inception of PMD would improve cow health, production, and fertility, and reduce economic loss. Therefore, the overall goal is to identify blood metabolites in overconditioned dry cows that could be used as predictors for pre-onset PMD.
Dairy cows fed a high plane of nutrition prepartum display an “overnutrition syndrome” with symptoms similar to diabetic or obese nonruminant animals (2). For example, overconditioned prepartum cows develop hyperglycemia and hyperinsulinemia prior to calving (2). This metabolic dysfunction caused by weight gain increases the severity of NEFA mobilization and BHBA production postpartum (2,9). Interestingly, excessive circulation of NEFA is accompanied by insulin resistance (17,18). As insulin is an antilipolytic hormone, insulin resistance stimulates lipolysis, thus providing a mechanism by which overconditioned cows generate more NEFA during the gestation to lactation transition relative to lean cows. Identifying predictors of pre-onset insulin resistance would be a beneficial approach to prevent the acceleration of NEFA mobilization in overconditioned transition cows.
Considerable progress has been made over the past two decades in the field of transition cow nutrition and management (19-21); however, PMD is still costly for cow health and well-being as well as producer profits. Current approaches to prevent the occurrence of PMD are inadequate. For example, one approach is to identify individual cows that have high concentrations of blood NEFA pre- and postpartum (22,23); however this protocol decelerates the progression of PMD but does not prevent the initial onset of hyperlipidemia. Other approaches to prevent PMD include monitoring body condition scores (BCS), feeding close-up diets, and supplementing with glucogenic precursors (19). These approaches have limited effectiveness in preventing PMD because they are employed to manage heterogeneous groups of animals and are not targeted to cows with elevated PMD risk (21). Clearly, new strategies to prevent the development of hyperlipidemia, especially in overconditioned cows, during the transition from gestation to lactation are warranted. One such progressive approach would be to identify cows at a markedly higher risk for developing PMD prior to the initial mobilization of NEFA. This advancement would give producers the power to modify individual cow or herd management plans prior to the onset of NEFA mobilization thus preventing future economic burden.
In human medicine, biomarker discovery using mass spectrometry-based metabolomics is at the forefront of research aimed at preventing insulin resistance caused by excessive weight gain (24-27). Ceramides and fatty acylcarnitines are two classes of metabolites that have been recognized as pre-onset indicators for the establishment of insulin resistance in overweight nonruminants (27-31). The levels of ceramides and fatty acylcarnitines increase in adipose tissue and blood, and are associated with the progression of insulin resistance, hyperlipidemia, and inflammation (32-35). Since overfed prepartum cows can experience similar metabolic symptoms, levels of ceramides and fatty acylcarnitines may also increase in circulation. Whether ceramides or fatty acylcarnitines could be used as biomarkers for pre-onset insulin resistance and hyperlipidemia in overconditioned transition dairy cows is unknown are addressed with this proposal.
OBJECTIVE 1: Identify prepartum blood biomarkers in overconditioned transition cows associated with reduced insulin sensitivity and elevated NEFA mobilization after calving. Employing cutting-edge mass spectrometry-based technologies, plasma collected from prepartum cows during the far-off period will be screened for unique metabolite signatures that can mark dairy cows at risk of developing greater reductions in insulin action and a greater magnitude of NEFA mobilization. Focus will be placed on ceramides and fatty acylcarnitines, adipose-derived lipid mediators associated with the development of insulin resistance and excessive lipolysis in overweight monogastrics. Monitoring biomarker levels prior to the onset of PMD might give dairy producers the ability to modify nutrition and management plans to maintain superior cow health through the transition from gestation to lactation.
OBJECTIVE 2: Inform and educate the dairy industry about approach, results, implications, and future direction. Presented findings at a national conference, and within a popular press extension article and peer-review publications.
Cooperators
Research
Experimental design and population
This trial was carried out at DoVan Farms, Inc. in Berlin, PA, a commercial herd with 700 lactating Holstein dairy cows and managed by the VanGilder family. At -30 d relative to calving, seventy multiparous transition cows were assigned to one of three groups using a 5-point BCS scale (1 = lean, 5 = obese): BCS < 3.0, BCS from 3.0 to 4.0, and BCS > 4.0 to represent low, medium, and high body condition, respectively. The three groups of healthy cows were balanced across expected calving date and parity. The farm has three barns with headlocks for housing cows during the far-off dry period (-60 d to -21 d relative to expected calving; freestall), the close-up dry period (-21 d prior to expected calving; bedded pack), and immediately postcalving (freestall). Cows were fed a far-off, close-up, and lactation total mixed ration (TMR) with formulated energy densities of 1.40, 1.55, and 1.70 NEL, Mcal/kg dry matter, respectively. Diets were given ad libitum to achieve a minimum of 10% daily feed refusals. All cows had ad libitum access to water. Two trained investigators recorded BCS weekly. From dry off (-60 d prior to expected calving) to four weeks postpartum, any changes in health status and medical treatments provided were recorded.
Sample collection and processing
Blood was collected from the coccygeal vein using 20-gauge needles on -45, -30, -15, -7, and +4 d relative to calving, immediately prior to feeding. For serum collection, blood was collected into BD Vacutainer tubes containing clot activator (Becton, Dickinson and Co., Franklin Lakes, NJ) and allowed to remain at room temperature for 1 h prior to centrifugation (1300 x g for 10 min, 20?C). For plasma collection, blood was collected into BD Vacutainer tubes with EDTA (Becton, Dickinson and Co.) and placed on ice for 20 min followed by plasma separation via centrifugation (2700 x g for 10 min, 4?C). Sera and plasma were removed and snap frozen in liquid nitrogen and stored at -80?C. Samples of TMR were collected once weekly, stored and frozen at -20?C. Milk was collected at +4 and +10 d relative to calving and shipped for analysis within 1 week of collection.
Sample analysis
Metabolomics analyses
A mass spectrometry-based approach was used to quantify plasma levels of ceramides and fatty acylcarnitines in overconditioned and lean cows. Nonesterified fatty acids serve as substrate for ceramide and fatty acylcarnitine synthesis; therefore, analysis of individual NEFA was performed for comparison. Following a methanol-chloroform extraction, extracts were analyzed using a gas chromatograph/triple quadrupole mass spectrometer (GC/MS/MS) and a high-performance liquid chromatography mass spectrometer (LC/MS/MS.). Profiling of ceramides and fatty acylcarnitines was performed using LC/MS/MS, while NEFA profiling was analyzed using GC/MS/MS technology. These targeted mass spectrometry methods use retention time, accurate mass, and isotopic patterns to detect specific metabolites. Quantitation of metabolites was achieved through stable-isotope dilution. All raw mass spectral data were normalized to internal standards to minimize false positives and variation.
NEFA quantification assay
Serum NEFA concentrations were measured using the HR Series, NEFA HR assay (Wako Chemicals USA, Inc., Richmond, VA) following the protocol provided by the manufacturer. The assay utilizes the formation of fatty acyl-CoA from NEFA, followed by the oxidation of fatty acyl-CoA to form hydrogen peroxide. The blue color change is detected and quantified. The concentration of NEFA in serum is expressed as mmol/l.
Insulin quantification assay
Plasma insulin concentrations were analyzed using a bovine insulin enzyme-linked immuno sorbent assay (ELISA; Mercodia, Uppsala, Sweden) following the manufacturer’s instructions. The concentration of insulin in plasma is expressed as μU/ml.
Glucose quantification assay
Plasma glucose concentrations were analyzed using the Wako Autokit Glucose Assay (Wako Chemicals USA, Inc.) following the protocol provided by the manufacturer. The assay is specific to β-D glucose and converts all forms of glucose to β-D glucose via a mutarotase to react with glucose oxidase for production of hydrogen peroxide. The red color change is detected and quantified. The concentration of glucose in plasma is expressed as mg/dl.
RQUICKI analysis
The RQUICKI is an effective method to monitor insulin sensitivity in dairy cows (36). The RQUICKI method incorporates basal measurements from circulating concentrations of insulin, glucose, and NEFA by this equation: RQUICKI= 1/[log (G) + log (I) + log (NEFA)], where G is the concentration of glucose in mg/dl, I is the concentration of insulin in μU/ml and NEFA is mmol/l. This indirect method, developed to estimate insulin sensitivity cows, has been used to demonstrate reduced insulin function following weight gain in dairy cows (36). Using data collected from the NEFA, insulin, and glucose quantification assays, the RQUICKI for cows was assessed at all sampling time points.
BHBA quantification assay
Plasma BHBA concentrations were analyzed using the Autokit 3H-B (Wako Chemicals USA, Inc.) following the manufacturer’s protocol. The assay initially breaks down acetoacetate into acetone and CO2, and then the conversion of BHBA into thio-NADH can be measured spectrophotometrically. The concentration of BHBA in plasma is expressed as μmol/l.
TMR analysis
Samples of TMR were composited at trial completion and analyzed for dry matter, nonstructural carbohydrates, acid detergent fiber, neutral detergent fiber, crude protein, and minerals by Cumberland Valley laboratory, Cumberland, MD.
Milk analysis
Samples of milk were analyzed for milk fat, protein, lactose, total solids, and SCC by Dairy One, Hagerstown, MD.
Statistical analysis
All data for plasma variables were analyzed as repeated measures over time relative to calving using ANOVA under the mixed procedure (SAS Institute, 2001). The statistical model included the random effect of cow nested within BCS and the fixed effects of BCS and day (relative to parturition). The most appropriate covariance structure for the repeated measures analysis was selected for each variable after evaluating 7 different covariance structures (variance components, first-order autoregressive, heterogeneous first-order autoregressive, compound symmetry, heterogeneous compound symmetry, first-order ante-dependence, and unstructured), and the structure with the smallest Akaike’s information criterion coefficient was selected for further analysis. The method of Kenward-Rogers was used for calculation of denominator degrees of freedom. Preplanned contrasts were used in order to evaluate differences between lean and overweight at each time point. Non-parametric Spearman’s rank-order correlations were performed, in order to determine associations between plasma metabolites and insulin sensitivity. Changes in body weight and BCS pre- and postpartum were analyzed under a generalized linear model with PROC GLM of SAS. All results are expressed as least square means and standard error of the means. Significance was declared at P< 0.05 and trends at P< 0.1.
Results
Body condition score was lower in lean, relative to overweight cows, for all the timepoints evaluated (P< 0.05), and a BCS x day relative to calving interaction was observed (P< 0.01). Relative to lean, overweight cows displayed accelerated BCS loss (P< 0.01), and tended to lose more body weight (P< 0.10) during prepartum. Body condition score had no effect milk yield or milk composition, however, changes over time were evident for these variables (P< 0.0001), as expected. A tendency for a day postpartum x BCS interaction was found for somatic cell score (P< 0.06), which was significantly higher in overweight cows at d 10 postpartum (P< 0.05).
Plasma glucose concentrations were affected by BCS, such that overweight cows had elevated glucose compared to lean cows at d -15 and -7 (P< 0.05), relative to calving. Insulin concentrations decreased sharply after d -15 prepartum (P< 0.0001), to be 54% lower by d 4 postpartum. Insulin concentrations were higher in overweight cows at d -30 and -15 (P< 0.01), and tended to be lower (P= 0.06) at d 4 postpartum. NEFA concentrations changed over peripartum (P< 0.0001), with an overall increase of 57, 144, and 283% for d -15, -7 and 4, respectively, relative to d -45. Overweight cows had higher NEFA concentrations at d -15 (P< 0.05), and tended to be higher at d -7 and 4 relative to calving (all P= 0.06). Overall insulin sensitivity, estimated through RQUICKI, changed during the peripartum (P< 0.0001) and decreased by 13% on d 4, relative to d -45. Compared to lean , insulin sensitivity in overweight cows tended to be lower (P= 0.07) as early as d -45, and was significantly lower at d -30, -15, -7 and 4, relative to calving (all P< 0.05). Plasma concentrations of BHBA were not affected by BCS, however, a significant effect of day relative to calving was observed (P< 0.01), and BHBA was 40% higher at d 4 postpartum, relative to d -30 prepartum.
Ceramide profiles were obtained for 6 ceramides classes, including simple ceramides (Cer), dihydroceramides (Dihydro-Cer), monohexylceramides (MHCer), dihydromonohexylceramides (Dihydro-MHCer), dihexylceramides (DHCer), and dihydrodihexylceramides (Dihydro-DHCer), with a total of 32 different species identified.
Plasma concentrations of several ceramide species were affected by time relative to calving (P< 0.05), with the lowest concentrations occurring mostly at d -30, and the highest concentrations at d 4 postpartum. Several simple ceramides were elevated in overweight cows, including C18:0, C20:0, and C24:1 Cer (P<0.05). Ceramides were negatively correlated with insulin sensitivity. Significant correlation coefficients (r) between ceramides and RQUICKI ranged from -0.33 to -0.21, (all P< 0.05), and included Cer with chain lengths ranging from 20C to 26C. As expected, NEFA correlated negatively with insulin sensitivity (r = 0.62; P< 0.05). Significant positive correlations between NEFA and several ceramides species were detected, ranging from 0.21 to 0.60 (all P< 0.05). The strongest correlations were observed for MH-Cer and Cer, and included some of the predominant ceramide species found in plasma.
Fatty acid and fatty acylcarnitine profiles were also generated; however, these data are still being analyzed. Initial analyses reveal increased levels of saturated fatty acids in overweight dairy cattle when compared with lean animals. Furthermore, fatty acylcarnitine profiles are modified in overweight cattle; however, these responses are acyl-chain length specific.
Discussion
The metabolic adaptations that occur during peripartum are key to allow the shift in the flow and use of nutrients to favor fetal growth and milk synthesis (36). Insulin secretion and insulin sensitivity (of adipose tissue only) are both expected to decrease during the peripartum in order to allow these changes (37). The RQUICKI calculation used in our study is one of several surrogate indexes commonly utilized to indirectly study insulin sensitivity (38; 39), and it has been recently validated to describe differences in apparent insulin sensitivity in ruminants (40). Changes in RQUICKI are expected to reflect changes in plasma glucose, NEFA and insulin concentrations. Our findings on the peripartal changes in plasma insulin (decreased), as well and NEFA and BHBA (increased) are consistent with the expected homeorhetic shift that allows increased lipolysis of adipose tissue and higher glucose flux to the gravid uterus and the mammary gland. Moreover, these changes occurred in concert with changes in whole-body insulin sensitivity, which decreased progressively during the peripartum. Of particular interest, the elevated plasma glucose, insulin and NEFA concentrations, exhibited by overweight cows, are suggestive of impaired whole-body insulin sensitivity, and was consistent with decreased RQUICKI values in these animals. Circulating NEFA are considered to mainly represent adipose tissue lipolysis (41), although some NEFA may be derived from non-specific lipolysis of circulating TG in mammary gland and other tissues (42). The elevation of plasma NEFA concentrations in overconditioned cows in our study is also consistent with the observation of increased body weight and BCS loss, indicative of increased lipolysis occurring in adipose tissue of these cows these cows. Together, these findings confirm our hypothesis that obese cows would display elevated plasma NEFA and concomitant impaired insulin sensitivity, supporting the link between adiposity and the development of insulin resistance in dairy cows.
Ceramides have gained increased attention during the past two decades because of their established involvement in the pathogenesis of type 1 and type 2 diabetes (43), and other metabolic disorders related to obesity. Using a mass spectrometry approach, we successfully fractionated several classes of ceramides and characterized their profiles in the bovine plasma during peripartum. Multiple studies using cultured cells, animal models and human subjects, have shown that ceramides play important roles in the progression of apoptosis of pancreatic β-cells, insulin resistance, and the reduction of insulin gene expression (44; 45; 46; 17) is consistent with the elevation in total plasma ceramides, monohexyl- and dihexyl-ceramides observed in our study, which occurred with simultaneous reductions in insulin sensitivity. The negative associations between the different classes of ceramides and insulin sensitivity, support the notion that ceramides can contribute to the homeorhetic development of insulin resistance that commonly occurs to favor milk synthesis during the transition period. This relationship may be more relevant in transition overweight cows, who are at a higher risk for PMD, and exhibited elevated ceramides with simultaneous lower insulin sensitivity, relative to lean cows. Importantly, because ceramides were elevated in overweight cows during prepartum, and these elevations correlated negatively to insulin sensitivity, the possibility arises to use specific ceramides as potential biomarkers of insulin resistance during the transition period.
Our data demonstrate an association between plasma ceramides and the development of insulin resistance in overweight transition dairy cattle, and future research should validate the efficacy of ceramides to predict for PMD.
In addition to achieving our research goal, we developed an Agricultural Research and Education Partnership between WVU and DoVan Farms. For research, DoVan farms will continue to be the off-campus site of dairy science research. To date we have completed four research studies with DoVan Farms with an additional three studies planned. DoVan Farms will also serve as our field laboratory for regional research in Somerset and Bedford counties in PA. Furthermore, we have developed an undergraduate internship program with DoVan Farms. DoVan Farms will host an undergraduate intern during each summer to train individuals to manage a large-scale dairy operation in the Northeast dairy region.
*Evidence for achieving Objective 2 is demonsted below within Publications/Outreach.
1) Contreras, G. A. and Sordillo, L. M. 2011. Lipid mobilization and inflammatory responses during the transition period of dairy cows. Comparative Immunology, Microbiology and Infectious Diseases 34:281-289.
2) Janovick, N. A., Boisclair, Y. R., and Drackley, J. K. 2011. Prepartum dietary energy intake affects metabolism and health during the periparturient period in primiparous and multiparous Holstein cows. Journal of Dairy Science 94:1385-1400.
3) Rukkwamsuk, T., Wensing, T., and Geelen, M. J. H. 1998. Effect of overfeeding during the dry period on regulation of adipose tissue metabolism in dairy cows during the periparturient period. Journal of Dairy Science 81:2904-2911
4) Rukkwamsuk, T., Kruip, T. A. M., Meijer, G. A. L., and Wensing, T. 1999. Hepatic fatty acid composition in periparturient dairy cows with fatty liver induced by intake of a high energy diet in the dry period. Journal of Dairy Science 82:280-287.
5) Lacetera, N. Scalia, D., Bernabucci, U., Ronchi, B., Pirazzi, D., and Nardone, A. 2005. Lymphocyte functions in overconditioned cows around parturition. Journal of Dairy Science 88:2010-2016.
6) Duffield, T. F., Lissemore, K. D., McBride, B. W., and Leslie, K. E. 2009. Impact of hyperketonemia in early lactation dairy cows on health and production. Journal of Dairy Science 92:571-580.
7) Bernabucci, U., Ronchi, B., Lacetera, N., and Nardone, A. 2005. Influence of body condition score on relationships between metabolic status and oxidative stress in periparturient dairy cows. Journal of Dairy Science 88:2017-2026.
8) O’Boyle, N., Corl, C. M., Gandy, J. C., and Sordillo, L. M. 2006. Relationship of body condition score and oxidant stress to tumor necrosis factor expression in dairy cattle. Veterinary Immunology and Immunopathology 113:297-304.
9) Dann, H. M. Litherland, N. B., Underwood, J. P., Bionaz, M., D'Angelo, A., McFadden, J. W., Drackley, J. K. 2006. Diets during far-off and close-up dry periods affect periparturient metabolism and lactation in multiparous cows. Journal of Dairy Science 89:3563-3577.
10) Bobe, G., Young, J. W., and Beitz, D. C. 2004. Invited Review: Pathology, Etiology, Prevention, and Treatment of Fatty Liver in Dairy Cows. Journal of Dairy Science 87:3105-3124.
11) Seifi, H. A., LeBlanc, S. J., Leslie, K. E., and Duffield, T. F. 2011. Metabolic predictors of postpartum disease and culling risk in dairy cattle. The Veterinary Journal 188:216-220.
12) Gröhn, Y. T., Eicker, S. W., Ducrocq, V., and Hertl, J. A. 1998. Effect of diseases on the culling of Holstein dairy cows in New York State. Journal of Dairy Science 81:966-978.
13) Heuer, C., Schukken, Y. H., and Dobbelaar, P. 1999. Postpartum body condition score and results from the first test day milk as predictors of disease, fertility, yield, and culling in commercial dairy herds. Journal of Dairy Science 82:295-304.
14) Geishauser, T., Leslie, K., Kelton, D., and Duffield, T. 2001. Monitoring for subclinical ketosis in dairy herds. Compendium S65-S71.
15) Fourichon, C., Seegers, H., Bareille, N., and Beaudeau, F. 1999. Effects of disease on milk production in the dairy cow: a review. Preventive Veterinary Medicine 41:1-35.
16) Westwood, C. T., Lean, I. J., and Garvin, J. K. 2002. Factors influencing fertility of Holstein dairy cows: A multivariate description. Journal of Dairy Science 85:3225-3227.
17) Kahn, S. E., Hull, R. L., and Utzschneider, K. M. 2006. Mechanisms linking obesity to insulin resistance and type 2 diabetes. Nature 444:840-846.
18) Holtenius, K., Agenas, S., Delavaud, C. and Chilliard, Y. 2003. Effects of feeding intensity during the dry period. 2. Metabolic and hormonal responses. Journal of Dairy Science 86:883-891.
19) Overton, T. and Waldron, M. 2004. Nutritional management of transition dairy cows: strategies to optimize metabolic health. Journal of Dairy Science 87:E105-E119.
20) Leblanc, S. 2010. Monitoring metabolic health of dairy cattle in the transition period. Journal of Reproduction and Development 56:S29-S35.
21) Ingvartsen, K. L. 2006. Feeding- and management-related diseases in the transition cow - Physiological adaptations around calving and strategies to reduce feeding-related diseases. Animal Feed Science and Technology 126:175-213.
22) Ospina, P. A., Nydam, D. V., Stokol, T., and Overton, T. R. 2010. Associations of elevated nonesterified fatty acids and β-hydroxybutyrate concentrations with early lactation reproductive performance and milk production in transition dairy cattle in the northeastern United States. Journal of Dairy Science 93:1596-1603.
23) Ospina, P. A., Nydam, D. V., Stokol, T., and Overton, T. R. 2010. Evaluation of nonesterified fatty acids and β-hydroxybutyrate in transition dairy cattle in the northeastern United States: Critical thresholds for prediction of clinical diseases. Journal of Dairy Science 93:546-554.
24) Kim, H.-J., Kim, J. H., Noh, S., Hur, H. J., Sung, M. J., Hwang, J., Park, J. H., Yang, H. J., Kim, M., and Kwon, D. Y. 2010. Metabolomic analysis of livers and serum from high-fat diet induced obese mice. Journal of Proteome Research 10:722-731.
25) German, J. B., Hammock, B. D., and Watkins, S. M. 2005. Metabolomics: building on a century of biochemistry to guide human health. Metabolomics 1:3-9.
26) Vinayavekhin, N., Homan, E. A., and Saghatelian, A. 2009. Exploring disease through metabolomics. ACS Chemical Biology 5:91-103.
27) Sampey, B. P., Freemerman, A. J., Zhang, J., Kuan, P., Galanko, J. A., O'Connell, T. M., Ilkayeva, O. R., Muehlbauer, M. J., Stevens, R. D., Newgard, C. B., Brauer, H. M., Troester, M. A., and Makowski, L. 2012. Metabolomic profiling reveals mitochondrial-derived lipid biomarkers that drive obesity-associated inflammation. PLoS One 7:e38812 1-10.
28) Haus, J. M. Kashyap, S. R., Kasumov, T., Zhang R., Kelly, K. R., Defronzo, R. A., and Kirwan, J. P. Plasma ceramides are elevated in obese subjects with type 2 diabetes and correlate with the severity of insulin resistance. Diabetes 58:337-343.
29) Kanety, H., Hemi, R., Papa, M. Z., and Karasik, A. 1996. Sphingomyelinase and ceramide suppress insulin-induced tyrosine phosphorylation of the insulin receptor substrate-1. Journal of Biological Chemistry 271:9895-9897.
30) Holland, W. L. and Summers, S. A. 2008. Sphingolipids, insulin resistance, and metabolic disease: new insights from in vivo manipulation of sphingolipid metabolism. Endocrine Reviews 29:381-402.
31) Mihalik, S. J. Goodpaster, B. H., Kelley, D. E., Chace, D. H., Vockely, J., Toledo, F. G., and DeLaney, J. P. 2010. Increased levels of plasma acylcarnitines in obesity and type 2 diabetes and identification of a marker of glucolipotoxicity. Obesity 18:1695-1700.
32) Kolak, M. Westerbacka, J., Velagapudi, V.R., Wågsäter, D., Yetukuri, L., Makkonen, J., Rissanen, A., Häkkinen, A. M., Lindell, M., Bergholm, R., Hamsten, A., Eriksson, P.,Fisher, R.M., Oresic, M., and Yki-Järvinen, H. 2007. Adipose tissue inflammation and increased ceramide content characterize subjects with high liver fat content independent of obesity. Diabetes 56:1960-1968.
33) Summers, S. A. 2006. Ceramides in insulin resistance and lipotoxicity. Progress in Lipid Research 45:42-72.
34) Muoio, D. M. and Newgard, C. B. 2008. Molecular and metabolic mechanisms of insulin resistance and β-cell failure in type 2 diabetes. Nature Reviews Molecular Cell Biology 9:193-205.
35) Adams, S. H., Hoppel, C. L., Lok, K. H., Wong, S. W., Minkler, P. E., Hwang, P. E., Newman, J. W., and Garvey, W. T. 2009. Plasma acylcarnitine profiles suggest incomplete long-chain fatty acid β-oxidation and altered tricarboxylic acid cycle activity in type 2 diabetic African-American women. The Journal of Nutrition 139:1073-1081.
35) Adams, S. H., Hoppel, C. L., Lok, K. H., Wong, S. W., Minkler, P. E., Hwang, P. E., Newman, J. W., and Garvey, W. T. 2009. Plasma acylcarnitine profiles suggest incomplete long-chain fatty acid β-oxidation and altered tricarboxylic acid cycle activity in type 2 diabetic African-American women. The Journal of Nutrition 139:1073-1081.
36) Bauman, D. E. and W.B. Currie. 1980. Partitioning of nutrients during pregnancy and lactation: A review of mechanisms involving homeostasis and homeorhesis. Journal of Dairy Science 63:1514-29
37) Bell, A.W. 1995. Regulation of organic nutrient metabolism during transition from late pregnancy to early lactation. Journal of Animal Science 73:2804-19.
38) Muniyappa, R., Lee, S., Chen, H., and Quon, M.J. 2008. Current approaches for assessing insulin sensitivity and resistance in vivo: advantages, limitations, and appropriate usage. American Journal of Physiology Endocrinology and Metabolism. 294:E15–E26
39) De Koster, J.D. and Opsomer, G. 2013. Insulin resistance in dairy cows. Vet. Clin. North Am. Food Anim. Pract. 2013; 29: 299–322
40) Holtenius, P. and Holtenius, K. 2007. A model to estimate insulin sensitivity in dairy cows. Acta Vet Scand. 49:29.
41) Dunshea, F.R., A.W. Bell, and T.E. Trigg. 1988. Relations between plasma non-esterified fatty acid metabolism and body fat mobilization during chronic undernutrition in goats. British Journal of Nutrition 60: 633-644.
42) Bickerstaffe, R., and E. F. Annison. 1974. The metabolism of glucose, acetate, lipids and amino acids in lactating dairy cows. Journal of Agricultural Science 82:71−85.
43) Garidel, P., Fölting, B., Schaller, I., and Kerth, A. 2010. The microstructure of the stratum corneum lipid barrier: mid-infrared spectroscopic studies of hydrated ceramide:palmitic acid:cholesterol model systems. Biophysical Chemistry 150 (1-3): 144–156
44) Shimabukuro, M., Y.T. Zhou, M. Levi, and R. H. Unger. 1998. Fatty acid induced b cell apoptosis: a link between obesity and diabetes. Proceedings of the National Academy of Sciences 95:2498–2502.
45) Summers, S.A., Garza, L.A., Zhou, H. and Birnbaum MJ. 1998. Regulation of insulin-stimulated glucose transporter GLUT4 translocation and Akt kinase activity by ceramide. Molecular and Cellular Biology.18:5457–5464
46) Kelpe, C.L., Moore, P.C., Parazzoli, S.D., Wicksteed, B., Rhodes, C.J. and Poitout, V. 2003. Palmitate inhibition of insulin gene expression is mediated at the transcriptional level via ceramide synthesis. Journal of Biological Chemistry. 278: 30015–21
Identifying prepartum dairy cows at risk of developing severe postpartum metabolic health would be a powerful method to improve dairy cow health, reduce economic loss, and improve the sustainability of the northeast dairy industry. Our analyses provide new insights into prepartum dairy cow health. Specifically, being able to identify novel relationships between metabolites and insulin resistance is an essential step in the development of a new diagnostic approach. Our research will serve as a stepping stone to a follow-up study aimed at determining the efficacy of newly discovered biomarkers (i.e. ceramides) to predict for PMD risk. Furthermore, we will assess whether newly discovered biomarkers are better predictors for PMD when compared with current biomarkers (e.g. NEFA and ketones). Such a diagnostic tool will allow producers to monitor disease progression in dairy cows with enhanced precision to improve cow health and production profits.
Education & Outreach Activities and Participation Summary
Participation Summary:
PRESENTATIONS
POSTERS:
Rico, J. E., N. J. Haughey, and J. W. McFadden. 2014. Plasma ceramides are elevated, and correlate with increased lipolysis and insulin resistance in an overconditioned transition cow model. Presented April 11th at Navigating Lipid Research, Carnegie Institution for Science.
Rico, J. E. and J. W. McFadden. 2014. Plasma ceramides are elevated in overconditioned transition cows, and correlate with increased lipolysis and severity of insulin resistance. Presented April 16th at Davis College, West Virginia University.
ORAL TALKS:
Rico, J. E. and J. W. McFadden. 2014. Overconditioned prepartum cows exhibit a greater magnitude of insulin resistance and mobilize more NEFA compared with lean cows. J. Dairy Sci. 97(E-Suppl. 1):335. Abstract (670). Presented July 22nd at ADSA/ASAS Joint Annual Meeting, Kansas City, MO.
Rico, J. E. and J. W. McFadden. 2014. Identifying biomarkers for pre-onset insulin resistance using mass spectrometry-based metabolomics: Plasma ceramides are elevated in overconditioned transition dairy cows. J. Dairy Sci. 97(E-Suppl. 1):336. Abstract (671). Presented July 22nd at ADSA/ASAS Joint Annual Meeting, Kansas City, MO.
Rico, J. E. and J. W. McFadden. 2014. Plasma sphingomyelins are correlated with plasma ceramides, and relate to severity of insulin resistance in overconditioned transition dairy cows. J. Dairy Sci. 97(E-Suppl. 1). Late-Breaking Abstract (8). Presented July 20th at ADSA/ASAS Joint Annual Meeting, Kansas City, MO.
McFadden, J. W. 2014. Ceramides and insulin resistance: Does a relationship exist in the dairy cow? Presented May 6th in Department of Biochemistry, West Virginia University School of Medicine, Morgantown, WV.
McFadden, J. W. 2014. West Virginia University Dairy Science Researchers Aim to Improve Dairy Herd Health in Somerset County, PA. Presented August 22nd to 29th at Somerset County Fair, Meyersdale, PA.
McFadden, J. W. 2014. Ceramides, a biomarker for insulin resistance in transition dairy cattle? Presented September 19th in Division of Animal and Nutritional Sciences, West Virginia University Davis College, Morgantown, WV.
Cokeley, R., J. E. Rico, and J. W. McFadden. 2015. Fatty acylcarnitine profiling in lean and overweight transition dairy cattle. (submitting abstract to 2015 ADSA/ASAS Joint Annual Meeting)
PUBLICATIONS
McFadden, J. W. 2014. Metabolomics: Can it advance detection of metabolic disease? Progressive Dairyman 28:73, October 19.
Rico, J. E., V. V. Bandaru, N. J. Haughey, and J. W. McFadden. 2014. Enhanced insulin resistance and lipolysis in overweight dairy cows transitioning from gestation to lactation is associated with elevated levels of plasma ceramides. J. Dairy Sci. (submitted)
Rico, J. E., V. V. Bandaru, N. J. Haughey, and J. W. McFadden. 2015. Plasma sphingomyelin levels are modified in dairy cows transitioning from gestation to lactation. J. Dairy Sci. (manuscript in progress)
Rico, J. E., V. V. Bandaru, N. J. Haughey, and J. W. McFadden. 2015. Plasma fatty acylcarnitine levels are associated with adiposity in dairy cows transitioning from gestation to lactation. J. Dairy Sci. (manuscript in progress)
Rico, J. E. 2016. The relationship between the sphingolipidome and insulin sensitivity in dairy cows transitioning from gestation to lactation. Ph.D. Dissertation (work in progress)
Project Outcomes
No economic analysis was conducted during this study.
Farmer Adoption
The current number of biomarkers for PMD is limited in dairy cattle (e.g. NEFA, BHBA). On-going education in the field is aimed at encouraging herd-level monitoring of NEFA and BHBA to monitor herd health and risk for PMD on a given farm. Further development and education is needed (see Areas Needing Additional Study) before we can recommend ceramide testing in transition dairy cows.
Areas needing additional study
1) We have identified ceramides and saturated fatty acids as biomarkers for insulin resistance and NEFA mobilization in transition dairy cows; however, their efficacy to detect for PMD remains to be determined. Initial evidence demonstrates that ceramides and saturated fatty acids are as predictive as total NEFA; however, the predictive potential of ceramides and saturated fatty acids should be determined across parity, breed, and herd management practices. Our study evaluated a small population of transition dairy cows (n=70; lean with BCS< 3.0, n=10; overweight with BCS> 4.0, n=11), a limitation for identifying relationships between metabolites and PMD frequency. 2) Collectively, we profiled 32 ceramides; however, we need to determine which specie(s) exhibit the greatest potential to predict for PMD. 3) We demonstrated that ceramides are associated with severity of insulin resistance; however, we don’t know whether ceramides mediate insulin resistance. On-going in our laboratory is addressing this question. 4) If ceramides do mediate insulin resistance, we need to identify mechanisms to lower ceramides in transition cows to improve insulin sensitivity, lower NEFA mobilization, and improve health. On-going in our laboratory is addressing this question. 5) In order for farmers to monitor ceramides and other biomarkers, regional diagnostic laboratories must be capable of profiling these metabolites at low cost. Methods can be refined to lower cost.