Journal of Veterinary Clinics (한국임상수의학회지)

The Korean Society of Veterinary Clinics (한국임상수의학회)
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Receiver Operating Characteristic Analysis for Prediction of Postpartum Metabolic Diseases in Dairy Cows in an Organic Farm in Korea

  • Kim, Dohee (College of Veterinary Medicine and Research Institute for Veterinary Science, Seoul National University) ;
  • Choi, Woojae (College of Veterinary Medicine and Research Institute for Veterinary Science, Seoul National University) ;
  • Ro, Younghye (College of Veterinary Medicine and Research Institute for Veterinary Science, Seoul National University) ;
  • Hong, Leegon (College of Veterinary Medicine and Research Institute for Veterinary Science, Seoul National University) ;
  • Kim, Seongdae (College of Veterinary Medicine and Research Institute for Veterinary Science, Seoul National University) ;
  • Yoon, Ilsu (College of Veterinary Medicine and Research Institute for Veterinary Science, Seoul National University) ;
  • Choe, Eunhui (Farm Animal Clinical Training and Research Center, Institute of Green-Bio Science and Technology, Seoul National University) ;
  • Kim, Danil (College of Veterinary Medicine and Research Institute for Veterinary Science, Seoul National University)
  • Received : 2022.06.20
  • Accepted : 2022.09.20
  • Published : 2022.10.31
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Abstract

Postpartum diseases should be predicted to prevent productivity loss before calving especially in organic dairy farms. This study was aimed to investigate the incidence of postpartum metabolic diseases in an organic dairy farm in Korea, to confirm the association between diseases and prepartum blood biochemical parameters, and to evaluate the accuracy of these parameters with a receiver operating characteristic (ROC) analysis for identifying vulnerable cows. Data were collected from 58 Holstein cows (16 primiparous and 42 multiparous) having calved for 2 years on an organic farm. During a transition period from 4 weeks prepartum to 4 weeks postpartum, blood biochemistry was performed through blood collection every 2 weeks with a physical examination. Thirty-one (53.4%) cows (9 primiparous and 22 multiparous) were diagnosed with at least one postpartum disease. Each incidence was 27.6% for subclinical ketosis, 22.4% for subclinical hypocalcemia, 12.1% for retained placenta, 10.3% for displaced abomasum and 5.2% for clinical ketosis. Between at least one disease and no disease, there were significant differences in the prepartum levels of parameters like body condition score (BCS), non-esterified fatty acid (NEFA), total bilirubin (T-bil), direct bilirubin (D-bil) and NEFA to total cholesterol (T-chol) ratio (p < 0.05). The ROC analysis of each of these prepartum parameters had the area under the curve (AUC) <0.7. However, the ROC analysis with logistic regression including all these parameters revealed a higher AUC (0.769), sensitivity (71.0%), and specificity (77.8%). The ROC analysis with logistic regression including the prepartum BCS, NEFA, T-bil, D-bil, and NEFA to T-chol ratio can be used to identify cows that are vulnerable to postpartum diseases with moderate accuracy.

Keywords

Acknowledgement

This study was partially supported by the Research Institute for Vet.erinary Science, Seoul National University.

References

  1. Asl AN, Nazifi S, Ghasrodashti AR, Olyaee A. Prevalence of subclinical ketosis in dairy cattle in the Southwestern Iran and detection of cutoff point for NEFA and glucose concentrations for diagnosis of subclinical ketosis. Prev Vet Med 2011; 100: 38-43. https://doi.org/10.1016/j.prevetmed.2011.02.013
  2. Bell AW. Regulation of organic nutrient metabolism during transition from late pregnancy to early lactation. J Anim Sci 1995; 73: 2804-2819. https://doi.org/10.2527/1995.7392804x
  3. Caixeta LS, Omontese BO. Monitoring and improving the metabolic health of dairy cows during the transition period. Animals (Basel) 2021; 11: 352. https://doi.org/10.3390/ani11020352
  4. Chamberlin WG, Middleton JR, Spain JN, Johnson GC, Ellersieck MR, Pithua P. Subclinical hypocalcemia, plasma biochemical parameters, lipid metabolism, postpartum disease, and fertility in postparturient dairy cows. J Dairy Sci 2013; 96: 7001-7013. https://doi.org/10.3168/jds.2013-6901
  5. Curtis CR, Erb HN, Sniffen CJ, Smith RD, Powers PA, Smith MC, et al. Association of parturient hypocalcemia with eight periparturient disorders in Holstein cows. J Am Vet Med Assoc 1983; 183: 559-561.
  6. Fadul M, Bogdahn C, Alsaaod M, Husler J, Starke A, Steiner A, et al. Prediction of calving time in dairy cattle. Anim Reprod Sci 2017; 187: 37-46. https://doi.org/10.1016/j.anireprosci.2017.10.003
  7. Ferguson JD, Galligan DT, Thomsen N. Principal descriptors of body condition score in Holstein cows. J Dairy Sci 1994; 77: 2695-2703. https://doi.org/10.3168/jds.S0022-0302(94)77212-X
  8. Goff JP. The monitoring, prevention, and treatment of milk fever and subclinical hypocalcemia in dairy cows. Vet J 2008; 176: 50-57. https://doi.org/10.1016/j.tvjl.2007.12.020
  9. Goff JP, Horst RL. Physiological changes at parturition and their relationship to metabolic disorders. J Dairy Sci 1997; 80: 1260-1268. https://doi.org/10.3168/jds.S0022-0302(97)76055-7
  10. Greiner M, Pfeiffer D, Smith RD. Principles and practical application of the receiver-operating characteristic analysis for diagnostic tests. Prev Vet Med 2000; 45: 23-41. https://doi.org/10.1016/S0167-5877(00)00115-X
  11. Grummer RR. Etiology of lipid-related metabolic disorders in periparturient dairy cows. J Dairy Sci 1993; 76: 3882-3896. https://doi.org/10.3168/jds.S0022-0302(93)77729-2
  12. Grummer RR. Impact of changes in organic nutrient metabolism on feeding the transition dairy cow. J Anim Sci 1995; 73: 2820-2833. https://doi.org/10.2527/1995.7392820x
  13. Herdt TH. Ruminant adaptation to negative energy balance. Influences on the etiology of ketosis and fatty liver. Vet Clin North Am Food Anim Pract 2000; 16: 215-230, v. https://doi.org/10.1016/S0749-0720(15)30102-X
  14. Holtenius P, Hjort M. Studies on the pathogenesis of fatty liver in cows. Bov pract 1990; 25: 91-94. https://doi.org/10.21423/bovine-vol0no25p91-94
  15. Jeong JK, Choi IS, Kang HG, Jung YH, Hur TY, Kim IH. Postpartum reproductive tract recovery and prevalence of health problems in dairy cows. J Vet Clin 2015; 32: 168-173. https://doi.org/10.17555/jvc.2015.04.32.2.168
  16. Jeong JK, Hur TY, Jung YH, Kang HG, Kim IH. Identification of predictive biomarkers of peri- and postpartum disorders in dairy cows. Korean J Vet Res 2019; 59: 1-8. https://doi.org/10.14405/kjvr.2019.59.1.1
  17. Kaneene JB, Miller R, Herdt TH, Gardiner JC. The association of serum nonesterified fatty acids and cholesterol, management and feeding practices with peripartum disease in dairy cows. Prev Vet Med 1997; 31: 59-72. https://doi.org/10.1016/S0167-5877(96)01141-5
  18. Katoh N. Relevance of apolipoproteins in the development of fatty liver and fatty liver-related peripartum diseases in dairy cows. J Vet Med Sci 2002; 64: 293-307. https://doi.org/10.1292/jvms.64.293
  19. Kayano M, Kataoka T. Screening for ketosis using multiple logistic regression based on milk yield and composition. J Vet Med Sci 2015; 77: 1473-1478. https://doi.org/10.1292/jvms.14-0691
  20. LeBlanc S. Monitoring metabolic health of dairy cattle in the transition period. J Reprod Dev 2010; 56 Suppl: S29-S35. https://doi.org/10.1262/jrd.1056S29
  21. LeBlanc SJ, Leslie KE, Duffield TF. Metabolic predictors of displaced abomasum in dairy cattle. J Dairy Sci 2005; 88: 159-170. https://doi.org/10.3168/jds.S0022-0302(05)72674-6
  22. McArt JAA, Nydam DV, Oetzel GR. Epidemiology of subclinical ketosis in early lactation dairy cattle. J Dairy Sci 2012; 95: 5056-5066. https://doi.org/10.3168/jds.2012-5443
  23. McSherry BJ, Lumsden JH, Valli VE, Baird JD. Hyperbilirubinemia in sick cattle. Can J Comp Med 1984; 48: 237-240.
  24. Mostafavi M, Seifi HA, Mohri M, Jamshidi A. Optimal thresholds of metabolic indicators of hepatic lipidosis in dairy cows. Revue Med Vet 2013; 164: 564-571.
  25. Ospina PA, Nydam DV, Stokol T, Overton TR. Evaluation of nonesterified fatty acids and beta-hydroxybutyrate in transition dairy cattle in the northeastern United States: critical thresholds for prediction of clinical diseases. J Dairy Sci 2010; 93: 546-554. https://doi.org/10.3168/jds.2009-2277
  26. Puppel K, Kuczynska B. Metabolic profiles of cow's blood; a review. J Sci Food Agric 2016; 96: 4321-4328. https://doi.org/10.1002/jsfa.7779
  27. Quiroz-Rocha GF, LeBlanc S, Duffield T, Wood D, Leslie KE, Jacobs RM. Evaluation of prepartum serum cholesterol and fatty acids concentrations as predictors of postpartum retention of the placenta in dairy cows. J Am Vet Med Assoc 2009; 234: 790-793. https://doi.org/10.2460/javma.234.6.790
  28. Reid IM, Harrison RD, Collins RA. Fasting and refeeding in the lactating dairy cow. 2. The recovery of liver cell structure and function following a six-day fast. J Comp Pathol 1977; 87: 253-265. https://doi.org/10.1016/0021-9975(77)90012-3
  29. Reinhardt TA, Lippolis JD, McCluskey BJ, Goff JP, Horst RL. Prevalence of subclinical hypocalcemia in dairy herds. Vet J 2011; 188: 122-124. https://doi.org/10.1016/j.tvjl.2010.03.025
  30. Ro Y, Choi W, Kim H, Kim D. Prepartal decrease in plasma total cholesterol concentration in dairy cows developed subclinical ketosis. J Vet Clin 2017; 34: 222-224. https://doi.org/10.17555/jvc.2017.06.34.3.222
  31. Rukkwamsuk T, Kruip TA, Wensing T. Relationship between overfeeding and overconditioning in the dry period and the problems of high producing dairy cows during the postparturient period. Vet Q 1999; 21: 71-77. https://doi.org/10.1080/01652176.1999.9694997
  32. Sepulveda-Varas P, Weary DM, Noro M, von Keyserlingk MA. Transition diseases in grazing dairy cows are related to serum cholesterol and other analytes. PLoS One 2015; 10: e0122317. https://doi.org/10.1371/journal.pone.0122317
  33. Van Saun RJ. Metabolic profiles for evaluation of the transition period. Am Assoc Bov Pract Conf Proc 2006; 39: 130-138.
  34. Wang Y, Huo P, Sun Y, Zhang Y. Effects of body condition score changes during peripartum on the postpartum health and production performance of primiparous dairy cows. Animals (Basel) 2019; 9: 1159. https://doi.org/10.3390/ani9121159