Prediction of postpartum performances of transition Zebu (Bos indicus) cows using receiver operating characteristics analysis

PRATIK RAMESH WANKHADE; AYYASAMY MANIMARAN; ARUMUGAM KUMARESAN; TAPAS K PATBANDHA; MUNIANDY SIVARAM; SAKTHIVEL JEYAKUMAR; DURAISAMY RAJENDRAN

doi:10.56093/ijans.v91i3.114142

Authors

PRATIK RAMESH WANKHADE Southern Regional Station, ICAR-National Dairy Research Institute, Bengaluru, Karnataka 560 030 India
AYYASAMY MANIMARAN Southern Regional Station, ICAR-National Dairy Research Institute, Bengaluru, Karnataka 560 030 India
ARUMUGAM KUMARESAN Southern Regional Station, ICAR-National Dairy Research Institute, Bengaluru, Karnataka 560 030 India
TAPAS K PATBANDHA Southern Regional Station, ICAR-National Dairy Research Institute, Bengaluru, Karnataka 560 030 India
MUNIANDY SIVARAM Southern Regional Station, ICAR-National Dairy Research Institute, Bengaluru, Karnataka 560 030 India
SAKTHIVEL JEYAKUMAR Southern Regional Station, ICAR-National Dairy Research Institute, Bengaluru, Karnataka 560 030 India
DURAISAMY RAJENDRAN Southern Regional Station, ICAR-National Dairy Research Institute, Bengaluru, Karnataka 560 030 India

https://doi.org/10.56093/ijans.v91i3.114142

Keywords:

Acute phase proteins, Deoni cows, Energy indicators, Inflammatory cytokines, Milk yield, Reproductive performance, Transition period

Abstract

Receiver Operating Characteristics (ROC) analysis is a popular method to discriminate between the two conditions of tested animals. In this study, we estimated accuracy and threshold values of metabolic (Dry matter Intake; DMI and Body Condition Score: BCS, NEFA and BHBA) and immune indicators (Haptoglobin: Hp, Serum Amyloid A: SAA, IL-6, TNF-a, IL-1b, and IL-8) during transition period (–21, –14, –7, 0, +3, +7, +14 and +21 days) to predict the high yielding (HY) and pregnant Deoni cows. ROC analysis revealed that SAA (–21 d), IL-6 (–21 and –7 d), BCS (–7 d) and BHBA (–7 d) during pre-partum period, differentiated HY from low or medium yielder (LY/MY) cows with moderate to excellent accuracy (AUC >0.8). During postpartum period, IL-6 (+7 d), TNF-a (+21 d), DMI (+21 d), NEFA (+14 d and +21 d) and BHBA (+21 d) levels had moderate to excellent accuracy to differentiate HY from LY or MY cows. IL-6 (–14 d and –7 d), TNF-a (–14 d) and DMI (–21 d; above 2 kg/100 kg BW) during pre-partum period while, SAA (+3 d and +7 d), IL-6 (+3 and +21 d) and TNF-a (+7 and +21 d) during postpartum period were significantly predicted the pregnant cows with moderate to excellent accuracy. Altogether, it is concluded that SAA, IL-6 and TNF-a levels had higher accuracy in discrimination of HY and pregnant cows from LY or MY and non-pregnant cows, respectively. Therefore, their corresponding threshold values could be used for predicting HY and pregnant Zebu (Deoni) cows.

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References

Alharthi A, Zhou Z, Lopreiato V, Trevisi E and Loor J J. 2018. Body condition score prior to parturition is associated with plasma and adipose tissue biomarkers of lipid metabolism and inflammation in Holstein cows. Journal of Animal Science and Biotechnology 9(1): 12. DOI: https://doi.org/10.1186/s40104-017-0221-1

Amadori M, Fusi F, Bilato D, Archetti I L, Lorenzi V and Bertocchi L. 2015. Disease risk assessment by clinical immunology analyses in periparturient dairy cows. Research in Veterinary Science 102: 25–26. DOI: https://doi.org/10.1016/j.rvsc.2015.07.002

Brodzki P, Kostro K, Brodzki A, Wawron W and Marczuk J. 2015. Inflammatory cytokines and acute-phase proteins concentrations in the peripheral blood and uterus of cows that developed endometritis during early postpartum. Theriogenology 84(1): 11–18. DOI: https://doi.org/10.1016/j.theriogenology.2015.02.006

Brown K L, Cassell B G, McGilliard M L, Hanigan M D and Gwazdauskas F C. 2012. Hormones, metabolites, and reproduction in Holsteins, Jerseys, and their crosses. Journal of Dairy Science 95(2): 698–707. DOI: https://doi.org/10.3168/jds.2011-4666

Chapinal N, Carson M E, LeBlanc S J, Leslie K E, Godden S, Capel M, Santos J E P, Overton M W and Duffield T F. 2012. The association of serum metabolites in the transition period with milk production and early-lactation reproductive performance. Journal of Dairy Science 95(3): 1301–09. DOI: https://doi.org/10.3168/jds.2011-4724

Colakoglu H E, Yazlik M O, Pekcan M, Kaya U, Kacar C, Vural M R, Kurt S, Yildirim M M, Bas A and Kuplulu S. 2019. Impact of prepartum body condition score loss on metabolic status during the transition period and subsequent fertility in Brown Swiss dairy cows. Journal of Veterinary Research 63(3): 375–82. DOI: https://doi.org/10.2478/jvetres-2019-0039

Das D N, Kataktalware M A, Ramesha K P and Reddy A O. 2011. Productive and reproductive performances of Deoni cattle under intensive management system. Indian Journal of Animal Sciences 81(11): 1186–88.

Dervishi E, Zhang G, Hailemariam D, Goldansaz S A, Deng Q, Dunn S M and Ametaj B N. 2016. Alterations in innate immunity reactants and carbohydrate and lipid metabolism precede occurrence of metritis in transition dairy cows. Research in Veterinary Science 104: 30–39. DOI: https://doi.org/10.1016/j.rvsc.2015.11.004

Drackley J K. 1999. Biology of dairy cows during the transition period: The final frontier. Journal of Dairy Science 82(11): 2259–73. DOI: https://doi.org/10.3168/jds.S0022-0302(99)75474-3

Gardner I A and Greiner M. 2006. Receiver operating characteristic curves and likelihood ratios: improvements over traditional methods for the evaluation and application of veterinary clinical pathology tests. Veterinary Clinical Pathology 35(1): 8–17. DOI: https://doi.org/10.1111/j.1939-165X.2006.tb00082.x

Garverick H A, Harris M N, Vogel-Bluel R, Sampson J D, Bader J, Lamberson W R, Spain J N, Lucy M C and Youngquist R S. 2013. Concentrations of non-esterified fatty acids and glucose in blood of periparturient dairy cows are indicative of pregnancy success at first insemination. Journal of Dairy Science 96(1): 181–88. DOI: https://doi.org/10.3168/jds.2012-5619

Gobikrushanth M, Macmillan K, Behrouzi A, Hoff B and Colazo M G. 2019. The factors associated with postpartum body condition score change and its relationship with serum analytes, milk production and reproductive performance in dairy cows. Livestock Science 228: 151–60. DOI: https://doi.org/10.1016/j.livsci.2019.05.016

Grummer R R, Mashek D G and Hayirli A.2004. Dry matter intake and energy balance in the transition period. Veterinary Clinics: Food Animal Practice 20(3): 447–470. DOI: https://doi.org/10.1016/j.cvfa.2004.06.013

Grummer R R. 1995. Impact of changes in organic nutrient metabolism on feeding the transition dairy cow. Journal of Animal Science 73(9): 2820–33. DOI: https://doi.org/10.2527/1995.7392820x

Guretzky N J, Carlson D B, Garrett J E and Drackley J K. 2006. Lipid metabolite profiles and milk production for Holstein and Jersey cows fed rumen-protected choline during the periparturient period. Journal of Dairy Science 89(1): 188– 200. DOI: https://doi.org/10.3168/jds.S0022-0302(06)72083-5

Hajian-Tilaki K. 2013. Receiver operating characteristic (ROC) curve analysis for medical diagnostic test evaluation. Caspian Journal of Internal Medicine 4(2): 627–35.

Ingvartsen K L and Andersen J B. 2000. Integration of metabolism and intake regulation: a review focusing on periparturient animals. Journal of Dairy Science 83(7): 1573–97. DOI: https://doi.org/10.3168/jds.S0022-0302(00)75029-6

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(3–4): 175–213. DOI: https://doi.org/10.1016/j.anifeedsci.2005.08.003

Ishikawa Y, Nakada K, Hagiwara K, Kirisawa R, Iwai H, Moriyoshi M and Sawamukai Y. 2004. Changes in interleukin- 6 concentration in peripheral blood of pre-and post-partum dairy cattle and its relationship to postpartum reproductive diseases. Journal of Veterinary Medical Science 66(11): 1403– 08. DOI: https://doi.org/10.1292/jvms.66.1403

Kerhli M E. 2015. Immunological dysfunction in periparturient cows: Evidence, causes and ramifications. Florida Ruminant Nutrition Symposium 14–29.

Kuhla B. 2020. Pro-inflammatory cytokines and hypothalamic inflammation: implications for insufficient feed intake of transition dairy cows. Animal 14(1): 65–77. DOI: https://doi.org/10.1017/S1751731119003124

LeBlanc S. 2010. Monitoring metabolic health of dairy cattle in the transition period. Journal of Reproduction and Development 56: 29–35. DOI: https://doi.org/10.1262/jrd.1056S29

Macmillan K, Gobikrushanth M, Behrouzi A, Lopez-Helguera I, Cook N, Hoff B and Colazo M G. 2020. The association of circulating prepartum metabolites, minerals, cytokines and hormones with postpartum health status in dairy cattle. Research in Veterinary Science 130: 126–132. DOI: https://doi.org/10.1016/j.rvsc.2020.03.011

Manimaran A, Kumaresan A, Jeyakumar S, Mohanty T K, Sejian V, Kumar N, Sreela L, Prakash M A, Mooventhan P, Anantharaj A, Das D N. 2016. Potential of acute phase proteins as predictor of postpartum uterine infections during transition period and its regulatory mechanism in dairy cattle. Veterinary World 9(1): 91–100. DOI: https://doi.org/10.14202/vetworld.2016.91-100

National Research Council (NRC) 2001. Nutrient Requirements of Dairy Cattle. 7th revised edition National Academy of Science, Washington, DC.

Ospina P A, Nydam D V, Stokol T and Overton T R. 2010a. Evaluation of non-esterified fatty acids and -hydroxybutyrate in transition dairy cattle in the northeastern United States: Critical thresholds for prediction of clinical diseases. Journal of Dairy Science 93(2): 546–54. DOI: https://doi.org/10.3168/jds.2009-2277

Ospina P A, Nydam D V, Stokol T and Overton T R. 2010b. Association between the proportion of sampled transition cows with increased non-esterified fatty acids and - hydroxybutyrate and disease incidence, pregnancy rate, and milk production at the herd level. Journal of Dairy Science 93(8): 3595–3601. DOI: https://doi.org/10.3168/jds.2010-3074

Patbandha T K, Mohanty T K, Layek S S, Kumaresan A and Behera K. 2012. Application of pre-partum feeding and social behaviour in predicting risk of developing metritis in crossbred cows. Applied Animal Behaviour Science 139(1–2): 10–17. DOI: https://doi.org/10.1016/j.applanim.2012.03.014

Patbandha T K, Mohanty T K, Layek S S, Kumaresan A, Behera K and Chand S. 2018. Association of Peri-partum Blood Energy Metabolites with Post-partum Puerperal Metritis in Crossbred Cows. Proceedings of the National Academy of Sciences, India Section B: Biological Sciences 88(1): 93–99. DOI: https://doi.org/10.1007/s40011-016-0737-8

Perez-Baez J, Risco C A, Chebel R C, Gomes G C, Greco L F, Tao S, Thompson I M, do Amaral B C, Zenobi M G, Martinez N and Staples C R. 2019. Association of dry matter intake and energy balance pre-partum and post-partum with health disorders post-partum: Part I. Calving disorders and metritis. Journal of Dairy Science 102(10): 9138–50. DOI: https://doi.org/10.3168/jds.2018-15878

Peterson A T, Papes M and Soberon J. 2008. Rethinking receiver operating characteristic analysis applications in ecological niche modeling. Ecological Modelling 213(1): 63–72. DOI: https://doi.org/10.1016/j.ecolmodel.2007.11.008

Roberts T, Chapinal N, LeBlanc S J, Kelton D F, Dubuc J and Duffield T F.2012. Metabolic parameters in transition cows as indicators for early-lactation culling risk. Journal of Dairy Science 95(6): 3057–63. DOI: https://doi.org/10.3168/jds.2011-4937

Roche J R, Friggens N C, Kay J K, Fisher M W, Stafford K J, Berry D P. 2009. Invited review: body condition score and its association with dairy cow productivity, health, and welfare. Journal of Dairy Science 92(12): 5769–5801. DOI: https://doi.org/10.3168/jds.2009-2431

Shin D H, Jeong J K, Choi I S, Moon S H, Lee S C, Kang H G, Park S B and Kim I H. 2018. Associations between serum haptoglobin concentration and peri-and postpartum disorders, milk yield, and reproductive performance in dairy cows. Livestock Science 213: 14–18. DOI: https://doi.org/10.1016/j.livsci.2018.04.015

Sordillo L M, Contreras G A and Aitken S L. 2009. Metabolic factors affecting the inflammatory response of periparturient dairy cows. Animal Health Research Reviews 10(1): 53–63. DOI: https://doi.org/10.1017/S1466252309990016

Sundrum A. 2015. Metabolic disorders in the transition period indicate that the dairy cow’s ability to adapt is overstressed. Animals 5(4): 978–1020. DOI: https://doi.org/10.3390/ani5040395

Trevisi E, Amadori M, Cogrossi S, Razzuoli E and Bertoni G. 2012. Metabolic stress and inflammatory response in high- yielding, periparturient dairy cows. Research in Veterinary Science 93(2): 695–704. DOI: https://doi.org/10.1016/j.rvsc.2011.11.008

Trevisi E, Jahan N, Bertoni G, Ferrari A and Minuti A. 2015. Pro- inflammatory cytokine profile in dairy cows: consequences for new lactation. Italian Journal of Animal Science 14(3): 285–92. DOI: https://doi.org/10.4081/ijas.2015.3862

Trevisi E, Moscati L and Amadori M. 2016. Disease-predicting and prognostic potential of innate immune responses to noninfectious stressors: human and animal models. In the Innate Immune Response to Noninfectious Stressors: 209–235. DOI: https://doi.org/10.1016/B978-0-12-801968-9.00009-X

Wankhade P R, Manimaran A, Kumaresan A, Jeyakumar S, Ramesha K P, Sejian V, Rajendran D and Varghese M R. 2017.

Metabolic and immunological changes in transition dairy cows: A review. Veterinary World 10(11): 1367–77. DOI: https://doi.org/10.14202/vetworld.2017.1367-1377

Wankhade P R, Manimaran A, Kumaresan A, Jeyakumar S, Sejian V, Rajendran D, Bagath M, Sivaram M, Ramesha K P and Varghese M R. 2019. Active immune system and dry matter intake during the transition period are associated with postpartum fertility in lactating Zebu cows. Livestock Science 228: 18–24. DOI: https://doi.org/10.1016/j.livsci.2019.07.020

Wankhade P R, Manimaran A, Kumaresan A, Jeyakumar S, Ramesha K P, Sejian V, Rajendran D, Bagath M and Sivaram M. 2018. Metabolism and immune status during transition period influences the lactation performance in Zebu (Bos indicus) cows. Indian Journal of Animal Sciences 88(9): 1064– 69.

Wisnieski L, Norby B, Pierce S J, Becker T and Sordillo L M. 2019. Prospects for predictive modeling of transition cow diseases. Animal Health Research Reviews 20(1): 19–30. DOI: https://doi.org/10.1017/S1466252319000112

Zhang G, Dervishi E and Ametaj B N. 2018. Milk fever in dairy cows is preceded by activation of innate immunity and alterations in carbohydrate metabolism prior to disease occurrence. Research in Veterinary Science 117: 167–77. DOI: https://doi.org/10.1016/j.rvsc.2017.12.008

Zhao W, Chen X, Xiao J, Chen X H, Zhang X F, Wang T, Zhen Y G and Qin G X. 2019. Prepartum body condition score affects milk yield, lipid metabolism, and oxidation status of Holstein cows. Asian-Australasian Journal of Animal Sciences 32(12): 1889–96. DOI: https://doi.org/10.5713/ajas.18.0817