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Potential of milk mid-IR spectra to predict metabolic status of cows through blood components and an innovative clustering approach

Published online by Cambridge University Press:  10 July 2018

C. Grelet Open the ORCID record for C. Grelet [Opens in a new window] ,
A. Vanlierde Open the ORCID record for A. Vanlierde [Opens in a new window] ,
M. Hostens Open the ORCID record for M. Hostens [Opens in a new window] ,
L. Foldager Open the ORCID record for L. Foldager [Opens in a new window] ,
M. Salavati Open the ORCID record for M. Salavati [Opens in a new window] ,
K. L. Ingvartsen Open the ORCID record for K. L. Ingvartsen [Opens in a new window] ,
M. Crowe ,
M. T. Sorensen Open the ORCID record for M. T. Sorensen [Opens in a new window] ,
E. Froidmont Open the ORCID record for E. Froidmont [Opens in a new window]  and
C. P. Ferris Open the ORCID record for C. P. Ferris [Opens in a new window]
...Show all authors
C. Grelet
Affiliation:
Walloon Agricultural Research Center (CRA-W), Gembloux, 5030, Belgium
A. Vanlierde
Affiliation:
Walloon Agricultural Research Center (CRA-W), Gembloux, 5030, Belgium
M. Hostens
Affiliation:
Ghent University, Merelbeke, 9820, Belgium
L. Foldager
Affiliation:
Department of Animal Science, Aarhus University, Tjele, 8830, Denmark Bioinformatics Research Centre, Aarhus University, Aarhus, 8000, Denmark
M. Salavati
Affiliation:
Royal Veterinary College (RVC), London, NW1 0TU, UK
K. L. Ingvartsen
Affiliation:
Department of Animal Science, Aarhus University, Tjele, 8830, Denmark
M. Crowe
Affiliation:
University College Dublin (UCD), Dublin, Ireland
M. T. Sorensen
Affiliation:
Department of Animal Science, Aarhus University, Tjele, 8830, Denmark
E. Froidmont
Affiliation:
Walloon Agricultural Research Center (CRA-W), Gembloux, 5030, Belgium
C. P. Ferris
Affiliation:
Agri-Food and Biosciences Institute (AFBI), Belfast, BT9 5PX, Northern Ireland
C. Marchitelli
Affiliation:
Research Center for Animal Production and Aquaculture (CREA), Roma, 00198, Italy
F. Becker
Affiliation:
Leibniz Institute for Farm Animal Biology (FBN), Dummerstorf, 18196, Germany
T. Larsen
Affiliation:
Department of Animal Science, Aarhus University, Tjele, 8830, Denmark
F. Carter
Affiliation:
University College Dublin (UCD), Dublin, Ireland
F. Dehareng*
Affiliation:
Walloon Agricultural Research Center (CRA-W), Gembloux, 5030, Belgium
GplusE Consortium
Affiliation:
Walloon Agricultural Research Center (CRA-W), Gembloux, 5030, Belgium Ghent University, Merelbeke, 9820, Belgium Department of Animal Science, Aarhus University, Tjele, 8830, Denmark Bioinformatics Research Centre, Aarhus University, Aarhus, 8000, Denmark Royal Veterinary College (RVC), London, NW1 0TU, UK University College Dublin (UCD), Dublin, Ireland Agri-Food and Biosciences Institute (AFBI), Belfast, BT9 5PX, Northern Ireland Research Center for Animal Production and Aquaculture (CREA), Roma, 00198, Italy Leibniz Institute for Farm Animal Biology (FBN), Dummerstorf, 18196, Germany
*
†E-mail: f.dehareng@cra.wallonie.be
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Abstract

Unbalanced metabolic status in the weeks after calving predisposes dairy cows to metabolic and infectious diseases. Blood glucose, IGF-I, non-esterified fatty acids (NEFA) and β-hydroxybutyrate (BHB) are used as indicators of the metabolic status of cows. This work aims to (1) evaluate the potential of milk mid-IR spectra to predict these blood components individually and (2) to evaluate the possibility of predicting the metabolic status of cows based on the clustering of these blood components. Blood samples were collected from 241 Holstein cows on six experimental farms, at days 14 and 35 after calving. Blood samples were analyzed by reference analysis and metabolic status was defined by k-means clustering (k=3) based on the four blood components. Milk mid-IR analyses were undertaken on different instruments and the spectra were harmonized into a common standardized format. Quantitative models predicting blood components were developed using partial least squares regression and discriminant models aiming to differentiate the metabolic status were developed with partial least squares discriminant analysis. Cross-validations were performed for both quantitative and discriminant models using four subsets randomly constituted. Blood glucose, IGF-I, NEFA and BHB were predicted with respective R2 of calibration of 0.55, 0.69, 0.49 and 0.77, and R2 of cross-validation of 0.44, 0.61, 0.39 and 0.70. Although these models were not able to provide precise quantitative values, they allow for screening of individual milk samples for high or low values. The clustering methodology led to the sharing out of the data set into three groups of cows representing healthy, moderately impacted and imbalanced metabolic status. The discriminant models allow to fairly classify the three groups, with a global percentage of correct classification up to 74%. When discriminating the cows with imbalanced metabolic status from cows with healthy and moderately impacted metabolic status, the models were able to distinguish imbalanced group with a global percentage of correct classification up to 92%. The performances were satisfactory considering the variables are not present in milk, and consequently predicted indirectly. This work showed the potential of milk mid-IR analysis to provide new metabolic status indicators based on individual blood components or a combination of these variables into a global status. Models have been developed within a standardized spectral format, and although robustness should preferably be improved with additional data integrating different geographic regions, diets and breeds, they constitute rapid, cost-effective and large-scale tools for management and breeding of dairy cows.

Keywords

Fourier transform mid-IR spectrometrydairy cattlepredictionbiomarkermetabolic clustering
Type
Research Article
Information
animal , Volume 13 , Issue 3 , March 2019 , pp. 649 - 658
Copyright
© The Animal Consortium 2018 

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Footnotes

a

List of authors within the GplusE consortium: Niamh McLoughlin, Alan Fahey, Elizabeth Matthews, Andreia Santoro, Colin Byrne, Pauline Rudd, Roisin O’Flaherty, Sinead Hallinan, Claire Wathes, Zhangrui Cheng, Ali Fouladi, Geoff Pollott, Dirk Werling, Beatriz Sanz Bernardo, Alistair Wylie, Matt Bell, Mieke Vaneetvelde, Kristof Hermans, Geert Opsomer, Sander Moerman, Jenne Dekoster, Hannes Bogaert, Jan Vandepitte, Leila Vandevelde, Bonny Vanranst, Johanna Hoglund, Susanne Dahl, Soren Ostergaard, Janne Rothmann, Mogens Krogh, Else Meyer, Charlotte Gaillard, Jehan Ettema, Tine Rousing, Federica Signorelli, Francesco Napolitano, Bianca Moioli, Alessandra Crisà, Luca Buttazzoni, Jennifer McClure, Daragh Matthews, Francis Kearney, Andrew Cromie, Matt McClure, Shujun Zhang, Xing Chen, Huanchun Chen, Junlong Zhao, Liguo Yang, Guohua Hua, Chen Tan, Guiqiang Wang, Michel Bonneau, Andrea Pompozzi, Armin Pearn, Arnold Evertson, Linda Kosten, Anders Fogh, Thomas Andersen, Matthew Lucey, Chris Elsik, Gavin Conant, Jerry Taylor, Nicolas Gengler, Michel Georges, Frédéric Colinet, Marilou Ramos Pamplona, Hedi Hammami, Catherine Bastin, Haruko Takeda, Aurelie Laine, Anne-Sophie Van Laere, Martin Schulze, Sergio Palma Vera.

References

Bagby, RM, Parker, JD and Taylor, GJ 1994. The twenty-item Toronto Alexithymia Scale—I. Item selection and cross-validation of the factor structure. Journal of Psychosomatic Research 38, 23–32.Google Scholar
Bastin, C, Gengler, N and Soyeurt, H 2011. Phenotypic and genetic variability of production traits and milk fatty acid contents across days in milk for Walloon Holstein first-parity cows. Journal of Dairy Sciences 94, 4152–4163.Google Scholar
Belay, TK, Dagnachew, BS, Kowalski, ZM and Ådnøy, T 2017. An attempt at predicting blood β-hydroxybutyrate from Fourier-transform mid-infrared spectra of milk using multivariate mixed models in Polish dairy cattle. Journal of Dairy Sciences 100, 6312–6326.Google Scholar
Bell, AW and Bauman, DE 1997. Adaptations of glucose metabolism during pregnancy and lactation. Journal of Mammary Gland Biology and Neoplasia 2, 265–278.Google Scholar
Beltman, ME, Forde, N, Furney, P, Carter, F, Roche, JF, Lonergan, P and Crowe, MA 2010. Characterisation of endometrial gene expression and metabolic parameters in beef heifers yielding viable or non-viable embryos on Day 7 after insemination. Reproduction, Fertility and Development 22, 987–999.Google Scholar
Bjerre-Harpøth, V, Friggens, NC, Thorup, VM, Larsen, T, Damgaard, BM, Ingvartsen, KL and Moyes, KM 2012. Metabolic and production profiles of dairy cows in response to decreased nutrient density to increase physiological imbalance at different stages of lactation. Journal of Dairy Science 95, 2362–2380.Google Scholar
Broutin, P 2015. Determination of the concentration of a component in one fluid of an animal by spectroscopic analysis of another fluid. Patent. Pub. No: WO/2015/055966. International Application No: PCT/FR2014/052650. Publication Date: 23.04.2015. Retrieved on 6 May 2018 from https://patentscope.wipo.int/search/en/detail.jsf?docId=WO2015055966.Google Scholar
Butler, WR 2000. Nutritional interactions with reproductive performance in dairy cattle. Animal Reproduction Science 60, 449–457.Google Scholar
Collard, BL, Boettcher, PJ, Dekkers, JC, Petitclerc, D and Schaeffer, LR 2000. Relationships between energy balance and health traits of dairy cattle in early lactation. Journal of Dairy Sciences 83, 2683–2690.Google Scholar
Davies, A and Fearn, T 2006. Back to basics: calibration statistics. Spectroscopy Europe 18, 31–32.Google Scholar
De Koster, J, Salavati, M, Grelet, C, Crowe, M, Opsomer, G and Foldager, L 2018. The GplusE Consortium, Hostens, M Metabolic clustering of cows, indicators of metabolic imbalance and association with production parameters (Submitted).Google Scholar
de Roos, APW, van den Bijgaart, HJCM, Hørlyk, J and de Jong, G 2007. Screening for subclinical ketosis in dairy cattle by Fourier transform infrared spectrometry. Journal of Dairy Sciences 90, 1761–1766.Google Scholar
Drackley, JK, Overton, TR and Douglas, GN 2001. Adaptations of glucose and long-chain fatty acid metabolism in liver of dairy cows during the periparturient period. Journal of Dairy Science 84, 100–112.Google Scholar
Duffield, TF, Kelton, DF, Leslie, KE, Lissemore, KD and Lumsden, JH 1997. Use of test day milk fat and milk protein to detect subclinical ketosis in dairy cattle in Ontario. The Canadian Veterinary Journal 38, 713–718.Google Scholar
Esposito, G, Irons, PC, Webb, EC and Chapwanya, A 2014. Interactions between negative energy balance, metabolic diseases, uterine health and immune response in transition dairy cows. Animal Reproduction Science 144, 60–71.Google Scholar
Ettema, JF and Østergaard, S 2006. Economic decision making on prevention and control of clinical lameness in Danish dairy herds. Livestock Science 102, 92–106.Google Scholar
Fenwick, MA, Llewellyn, S, Fitzpatrick, R, Kenny, D A, Murphy, JJ, Patton, J and Wathes, DC 2008. Negative energy balance in dairy cows is associated with specific changes in IGF-binding protein expression in the oviduct. Reproduction 135, 63–75.Google Scholar
Gelé, M, Ferrand-Calmels, M, Brun-Lafleur, L, Werner, A and Gollé-Leidreiter, F 2015. Predicting the risk of ketosis using mid infrared spectrometry, ICAR Technical Series no. 19, 19.Google Scholar
Gengler, N, Soyeurt, H, Dehareng, F, Bastin, C, Colinet, C, Hammami, H, Vanrobays, ML, Lainé, A, Vanderick, A, Grelet, C, Vanlierde, A and Froidmont, E 2016. Capitalizing on fine milk composition for breeding and management of dairy cows. Journal of Dairy Sciences 99, 4071–4079.Google Scholar
Gengler, N, GplusE Consortium 2017. Targeted combination of estimated breeding values for lower accuracy mid-infrared biomarkers increases their usefulness in genetic evaluation of dairy cattle. In Proceedings of ICAR 2017, 15 June 2017, Edimburg, UK, pp. 42.Google Scholar
Grelet, C, Fernández Pierna, JA, Dardenne, P, Baeten, V and Dehareng, F 2015. Standardization of milk mid-infrared spectra from a European dairy network. Journal of Dairy Sciences 98, 2150–2160.Google Scholar
Grelet, C, Bastin, C, Gelé, M, Davière, J B, Johan, M, Werner, A, Reding, R, Fernandez Pierna, JA, Colinet, FG, Dardenne, P, Gengler, N, Soyeurt, H and Dehareng, F 2016. Development of Fourier transform mid-infrared calibrations to predict acetone, β-hydroxybutyrate and citrate contents in bovine milk through a European dairy network. Journal of Dairy Sciences 99, 4816–4825.Google Scholar
Grelet, C, Fernandez Pierna, JA, Dardenne, P, Soyeurt, H, Vanlierde, A, Colinet, F, Bastin, C, Gengler, N, Baeten, V and Dehareng, F 2017. Standardization of milk mid-infrared spectrometers for the transfer and use of multiple models. Journal of Dairy Sciences 100, 7910–7921.Google Scholar
Hammon, DS, Evjen, IM, Dhiman, TR, Goff, JP and Walters, JL 2006. Neutrophil function and energy status in Holstein cows with uterine health disorders. Veterinary Immunology and Immunopathology 113, 21–29.Google Scholar
Herdt, TH 2000. Ruminant adaptation to negative energy balance: influences on the etiology of ketosis and fatty liver. Veterinary Clinics of North America: Food Animal Practice 16, 215–230.Google Scholar
Ingvartsen, KL 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.Google Scholar
Ingvartsen, KL, Dewhurst, RJ and Friggens, NC 2003. On the relationship between lactational performance and health: is it yield or metabolic imbalance that cause production diseases in dairy cattle? A position paper. Livestock Production Science 83, 277–308.Google Scholar
LeBlanc, S 2010. Monitoring metabolic health of dairy cattle in the transition period. Journal of Reproduction and Development 56, S29–S35.Google Scholar
McArt, JAA, Nydam, DV and Overton, MW 2015. Hyperketonemia in early lactation dairy cattle: a deterministic estimate of component and total cost per case. Journal of Dairy Sciences 98, 2043–2054.Google Scholar
McParland, S, Banos, G, Wall, E, Coffey, MP, Soyeurt, H, Veerkamp, RF and Berry, DP 2011. The use of mid-infrared spectrometry to predict body energy status of Holstein cows. Journal of Dairy Sciences 95, 1222–1239.Google Scholar
McParland, S, Lewis, E, Kennedy, E, Moore, SG, McCarthy, B, O’Donovan, M, Butler, ST, Pryce, JE and Berry, DP 2014. Mid-infrared spectrometry of milk as a predictor of energy intake and efficiency in lactating dairy cows. Journal of Dairy Sciences 97, 5863–5871.Google Scholar
Moyes, KM, Drackley, JK, Morin, DE, Rodriguez-Zas, SL, Everts, RE, Lewin, HA and Loor, JJ 2010. Mammary gene expression profiles during an intramammary challenge reveal potential mechanisms linking negative energy balance with impaired immune response. Physiological Genomics 41, 161–170.Google Scholar
Moyes, KM, Larsen, T and Ingvartsen, KL 2013. Generation of an index for physiological imbalance and its use as a predictor of primary disease in dairy cows during early lactation. Journal of Dairy Science 96, 2161–2170.Google Scholar
Ospina, PA, Nydam, DV, Stokol, T and Overton, TR 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.Google Scholar
Rousseeuw, PJ, Debruyne, M, Engelen, S and Hubert, M 2006. Robustness and outlier detection in chemometrics. Critical Reviews in Analytical Chemistry 36, 221–242.Google Scholar
Salavati, M, Genotype plus Environment Consortium 2017. Investigating metabolic phenotypes in multiparous dairy cows by component analysis and clustering. In Proceedings of the 68th Annual Meeting of the European Federation of Animal Science, 28–31 August 2017, Tallin, Estonia, pp. 404.Google Scholar
Shetty, N, Løvendahl, P, Lund, MS and Buitenhuis, AJ 2017. Prediction and validation of residual feed intake and dry matter intake in Danish lactating dairy cows using mid-infrared spectroscopy of milk. Journal of Dairy Sciences 100, 253–264.Google Scholar
Suthar, VS, Canelas-Raposo, J, Deniz, A and Heuwieser, W 2013. Prevalence of subclinical ketosis and relationships with postpartum diseases in European dairy cows. Journal of Dairy Sciences 96, 2925–2938.Google Scholar
Turk, R, Juretic, D, Geres, D, Turk, N, Rekic, B, Simeon-Rudolf, V and Svetina, A 2004. Serum paraoxonase activity and lipid parameters in the early postpartum period of dairy cows. Research in Veterinary Science 76, 57–61.Google Scholar
Wathes, DC, Cheng, Z, Chowdhury, W, Fenwick, M A, Fitzpatrick, R, Morris, DG, Patton, J and Murphy, JJ 2009. Negative energy balance alters global gene expression and immune responses in the uterus of postpartum dairy cows. Physiological Genomics 39, 1–13.Google Scholar
Wathes, DC, Fenwick, M, Cheng, Z, Bourne, N, Llewellyn, S, Morris, DG, Kenny, R, Murphy, J and Fitzpatrick, R 2007. Influence of negative energy balance on cyclicity and fertility in the high producing dairy cow. Theriogenology 68, 232–241.Google Scholar