Ketosis in Dairy Cows during Early Lactation - Detection in Pooled Blood Serum Samples


  • Nenad Staničkov Department of veterinary medicine, Faculty of Agriculture, University of Novi Sad, Serbia
  • Marko Cincović Department of veterinary medicine, Faculty of Agriculture, University of Novi Sad
  • Radojica Djokovic Faculty of Agronomy Čačak, University of Kragujevac, Serbia
  • Branislava Belić Department of veterinary medicine, Faculty of Agriculture, University of Novi Sad, Serbia
  • Mira Majkić Department of veterinary medicine, Faculty of Agriculture, University of Novi Sad, Serbia
  • Maja Došenović Marinković Department of veterinary medicine, Faculty of Agriculture, University of Novi Sad, Serbia
  • Miloš Petrović Faculty of Agronomy Čačak, University of Kragujevac, Serbia
  • Dražen Kovačević Department of veterinary medicine, Faculty of Agriculture, University of Novi Sad, Serbia
  • Bojan Blond



Background: Ketosis is the most important metabolic disease with prevalence from 15 to 45%. Ketosis is diagnosed using a metabolic profile. Due to the high prevalence, it is necessary to determine a large number of metabolic profiles within farm, which represents an additional cost, so the implementation of pooled serum in assessing the metabolic status of cows was examined. The aim of this study was to validate and evaluate the influence of the relative position (Z-score) of the value of pooled sample metabolic parameters within the known reference value of healthy cows in the detection of ketosis in herd during early lactation.

Materials, Methods & Results: The experiment has been carried out using  blood samples collected by puncture of coccygeal vein from 50 ketotic and 50 healthy cows. Laboratory analysis includes determination of beta-hydroxybutyrate-BHB, non-esterified fatty acids-NEFA, cholesterol-CHOL, triglycerides-TGC, glucose-GLU, albumin-ALB, total protein-TPROT, UREA, Ca, P, total bilirubin-TBIL and aspartat aminotransferase-AST. The pooled serum was made from 10 individual samples originating from 10 different cows. A serum aliquot of 0.1 mL was taken from each sample, and a 1 mL volume of pooled serum was finally formed. Three types of serum pools were made: 1) 30 pooled sample were from ketosis; 2) 30 pooled sample were from healthy cows and 3) 60 pooled samples containing mixed sera of healthy cows and cows with ketosis were made as follows: 10 pools contain 10% to 60% of ketotic cows (1/10 to 6/10 samples). Statistical analysis includes: a) difference in metabolite concentration and Z-score in pooled sample and arithmetic mean individual sample in healthy and ketotic cow, b) correlation between Z-score of pooled sample and arithmetic mean of individual sample, c) ability of Z-score of metabolite to divide ketotic from healthy cow, d) correlation between Z-score and % of ketotic cow in pooled sample; and e) calculation of 95%CI of pooled sample Z-scores for each % of ketotic cow in pools. Z-score and all analysis were calculated for each metabolic parameter. The results of the study show that the mean values and Z-scores of the pool and the calculated average value of the individual samples participating in that pool differ significantly in healthy cows and cows in ketosis, except for TPROT and Ca. A higher value and a higher Z-score were found for BHB, NEFA, UREA, TBIL and AST, and a lower value and a lower Z-score for TGC, CHOL, GLU, ALB and P in ketotic cows compared to healthy cows. The value of the Z-score of the pooled sample and the calculated mean values of individual samples participating in the pool are highly correlated with each other (coefficient of determination over 99%). Z-score of metabolites in the pooled sample can be used to distinguish healthy from ketotic cows (ROC AUC= 0.711 to 0.989), except for TPROT and Ca. The Z-score value of the pooled sample shows a linear correlation with the percentage of ketotic cows in the pool and the reference ranges of Z-scores change significantly as a function of the percentage of ketosis cows.

Discussion: Modern research on the metabolic profile in cows requires obtaining a large amount of information from as few samples as possible. The advantages of using the Z-score are reflected in the following: this score does not depend on the absolute value of the metabolite, but on the position within the known population reference value, Z-score of sample and the arithmetic mean of individual samples included in the same pool are almost identical, the Z-score of these 2 groups of results is ideally correlated, the Z-score significantly correlates with the % of ketosis samples in the pooled sample. The use of pooled sample Z-score can be a useful in a herd level assessment of metabolic status and detection of ketosis as most important metabolic disease in dairy cows.

Keywords: dairy cattle, ketosis, metabolic disease, metabolic profile, pooled serum, z-score, diagnostics.


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Belić B. & Cincović M.R. 2020. Reference value of blood parameters in cattle. In: Reference Values of Laboratory Blood Parameters in Animals. Novi Sad (in Serbian): Faculty of Agriculture-Department of Veterinary Medicine, pp.40-110.

Belić B., Cincović M., Lakić I., Đoković R., Petrović M., Ježek J. & Starič J. 2018. Metabolic status of dairy cows grouped by anabolic and catabolic indicators of metabolic stress in early lactation. Acta Scientiae Veterinariae. 46: 1607. 9p.

Berge A.C. & Vertenten G. 2014. A field study to determine the prevalence, dairy herd management systems, and fresh cow clinical conditions associated with ketosis in western European dairy herds. Journal of Dairy Science. 97: 2145-2154.

Cao Y., Zhang J., Yang W., Xia C., Zhang H.Y., Wang Y.H. & Xu C. 2017. Predictive value of plasma parameters in the risk of postpartum ketosis in dairy cows. Journal of Veterinary Research. 61: 91-95.

Chuang X.U., Tai-yu S., Yuan Y.A.O., Hong-jiang Y., Cheng X.I.A. & Hong-you Z. 2016. Blood clinicopathological differences between type I and II ketosis in dairy cows. Indian Journal of Animal Research. 50(5): 753-758

Cincović M.R., Belić B., Radojičić B., Hristov S. & Đoković R. 2012. Influence of lipolysis and ketogenesis to metabolic and hematological parameters in dairy cows during periparturient period. Acta Veterinaria Beograd. 62(4): 429-444.

Cincović M.R., Delić-Vujanović B., Đoković R., Belić B., Blond B., Grubač S., Krnjaić S. & Majkić M. 2021. Multiparametric analysis of blood parameters and hyperketonemia in cows. Acta Agriculturae Serbica. 26(52): 137-143.

Delić B., Belić B., Cincović M.R., Djokovic R. & Lakić I. 2020. Metabolic adaptation in first week after calving and early prediction of ketosis type I and II in dairy cows. Large Animal Review. 26(2): 51-55.

Deniz A., Aksoy K. & Metin M. 2020. Transition period and subclinical ketosis in dairy cattle: association with milk production, metabolic and reproductive disorders and economic aspects. Medycyna Weterynaryjna. 76(09): 495-502

Djoković R., Dosković V., Cincović M., Belić B., Fratrić N., Jašović B. & Lalović M. 2017. Estimation of Insulin Resistance in Healthy and Ketotic Cows during an Intravenous Glucose Tolerance Test. Pakistan Veterinary Journal. 37(4): 387-392.

Đoković R., Ilić Z., Kurćubić V., Petrović M., Cincović M., Petrović M.P. & Caro-Petrović V. 2019. Diagnosis of subclinical ketosis in dairy cows. Biotechnology in Animal Husbandry. 35(2): 111-125.

Hakama M., Hakulinen T., Kenward M.G., Aaran R.K., Aromaa A., Knekt P., Nikkari T., Teppo L. & Peto R. 2004. Blood biochemistry and the risk of cancer Effect of sample pooling. Acta Oncologica. 43(7): 667-674.

Hosmer D.W. & Lemeshow S. 2000. Applied Logistic Regression. 2nd edn. New York: John Wiley and Sons, pp.160-164.

Hussein H.A., Westphal A. & Staufenbiel R. 2013. Pooled serum sample metabolic profiling as a screening tool in dairy herds with a history of ketosis or milk fever. Comparative Clinical Pathology. 22(6): 1075-1082.

Ježek J., Cincović M.R., Nemec M., Belić B., Djoković R., Klinkon M. & Starič J. 2017. Beta-hydroxybutyrate in milk as screening test for subclinical ketosis in dairy cows. Polish Journal of Veterinary Sciences. 20(3): 507-512.

Kessel S., Stroehl M., Meyer H.H.D., Hiss S., Sauerwein H., Schwarz F.J. & Bruckmaier R.M. 2008. Individual variability in physiological adaptation to metabolic stress during early lactation in dairy cows kept under equal conditions. Journal of Animal Science. 86(11): 2903-2912.

Kovačević D., Cincović M., Belić B., Đoković R. & Majkić M. 2021. Blood Serum Stability Limit and Maximum Storage Time of Bovine Samples. Acta Scientiae Veterinariae. 49: 1815. 9p.

Kovačević V., Cincović M.R., Belić B., Đoković R., Lakić I., Radinović M. & Potkonjak A. 2021. Biological variations of hematologic and biochemical parameters in cows during early lactation. Polish Journal of Veterinary Science. 24(1): 119-125.

Lei M.A.C. & Simões J. 2021. Invited Review: Ketosis Diagnosis and Monitoring in High-Producing Dairy Cows. Dairy. 2(2): 303-325.

Maity S., Rubić I., Kuleš J., Horvatić A., Đuričić D., Samardžija M., Ljubić B.B., Turk R., Gračner D., Maćešić N., Valpotić H. & Mrljak V. 2021. Integrated Metabolomics and Proteomics Dynamics of Serum Samples Reveals Dietary Zeolite Clinoptilolite Supplementation Restores Energy Balance in High Yielding Dairy Cows. Metabolites. 11(12): 842.

Martias C., Baroukh N., Mavel S., Blasco H., Lefèvre A., Roch L., Montigny F., Gatien J., Schibler L., Dufour-Rainfray D., Nadal-Desbarats L. & Emond P. 2021. Optimization of Sample Preparation for Metabolomics Exploration of Urine, Feces, Blood and Saliva in Humans Using Combined NMR and UHPLC-HRMS Platforms. Molecules. 26(14): 4111.

Nowroozi Asl A., Nazifi S., Rowshan Ghasrodashti A. & Olyaee A. 2011. 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. Preventive Veterinary Medicine. 100(1): 38-43.

Oetzel G.R. 2004. Monitoring and testing dairy herds for metabolic disease. Veterinary Clinics of North America: Food Animal Practice. 20: 651-674.

Oetzel G.R. 2007. Herd-level ketosis-diagnosis and risk factors. In: Proceedings of the 40th annual conference of bovine practitioners (Vancouver, Canada). pp.67-91.

Ospina P.A., Nydam D.V., Stokol, T. & 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(2): 546-554.

Petrović M.Ž., Cincović M., Starič J., Djoković R., Belić B., Radinović M., Majkić M. & Ilić Z.Ž. 2022. The Correlation between Extracellular Heat Shock Protein 70 and Lipid Metabolism in a Ruminant Model. Metabolites. 12(1): 19.

Pralle R.S., Weigel K.W. & White H.M. 2018. Predicting blood β-hydroxybutyrate using milk Fourier transform infrared spectrum, milk composition, and producer-reported variables with multiple linear regression, partial least squares regression, and artificial neural network. Journal of Dairy Science. 101(5): 4378-4387.

Schmitt R., Pieper L., Gonzalez-Grajales L.A., Swinkels J., Gelfert C.C. & Staufenbiel R. 2021. Evaluation of different acute-phase proteins for herd health diagnostics in early postpartum Holstein Friesian dairy cows. Journal of Dairy Research. 88(1): 33-37.

Steeneveld W., Amuta P., van Soest F.J., Jorritsma R. & Hogeveen H. 2020. Estimating the combined costs of clinical and subclinical ketosis in dairy cows. PloS one. 15(4): e0230448.

Sturm V., Efrosinin D., Öhlschuster M., Gusterer E., Drillich M. & Iwersen M. 2020. Combination of Sensor Data and Health Monitoring for Early Detection of Subclinical Ketosis in Dairy Cows. Sensors. 20(5): 1484.

Sun Y., Wang B., Shu S., Zhang H., Xu C., Wu L. & Xia C. 2015. Critical thresholds of liver function parameters for ketosis prediction in dairy cows using receiver operating characteristic (ROC) analysis. Veterinary Quarterly. 35: 159-164.

Uyarlar C., Çetingül S., Gültepe E.E., Sial A.R. & Bayram İ. 2018. Effects of Subclinical and Clinical Ketosis on The Incidence of Mastitis, Metritis, Culling Rate and Some Hematological Parameters in Dairy Cows. Kocatepe Veterinary Journal. 11(2): 186-193.

Van Saun R.J. 2007. Application of a pooled sample metabolic profile for use as a herd screening tool. In: Proceedings Danske Kvægfagdyrlægers Årsmøde - Danish bovine practitioner seminar (Middlefart, Denmark). pp.24-25.

Vanholder T., Papen J., Bemers R., Vertenten G. & Berge A.C.B. 2015. Risk factors for subclinical and clinical ketosis and association with production parameters in dairy cows in the Netherlands. Journal of Dairy Science. 98(2): 880-888.

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Staničkov, N., Cincović, M., Djokovic, R., Belić, B., Majkić, M., Došenović Marinković, M., Petrović, M., Kovačević, D., & Blond, B. (2022). Ketosis in Dairy Cows during Early Lactation - Detection in Pooled Blood Serum Samples. Acta Scientiae Veterinariae, 50.




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