Diagnostic milk biomarkers for predicting the metabolic health status of dairy cattle during early lactation
ABSTRACT: Data on metabolic profiles of blood sampled at d 3, 6, 9, and 21 in lactation from 117 lactations (99 cows) were used for unsupervised k-means clustering. Blood metabolic parameters included β-hydroxybutyrate (BHB), nonesterified fatty acids, glucose, insulin-like growth factor-1 (IGF-1) a...
| Main Authors: | , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2023-01-01
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| Series: | Journal of Dairy Science |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S0022030222006452 |
| _version_ | 1797980398398996480 |
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| author | S. Heirbaut X.P. Jing B. Stefańska E. Pruszyńska-Oszmałek L. Buysse P. Lutakome M.Q. Zhang M. Thys L. Vandaele V. Fievez |
| author_facet | S. Heirbaut X.P. Jing B. Stefańska E. Pruszyńska-Oszmałek L. Buysse P. Lutakome M.Q. Zhang M. Thys L. Vandaele V. Fievez |
| author_sort | S. Heirbaut |
| collection | DOAJ |
| description | ABSTRACT: Data on metabolic profiles of blood sampled at d 3, 6, 9, and 21 in lactation from 117 lactations (99 cows) were used for unsupervised k-means clustering. Blood metabolic parameters included β-hydroxybutyrate (BHB), nonesterified fatty acids, glucose, insulin-like growth factor-1 (IGF-1) and insulin. Clustering relied on the average and range of the 5 blood parameters of all 4 sampling days. The clusters were labeled as imbalanced (n = 42) and balanced (n = 72) metabolic status based on the values of the blood parameters. Various random forest models were built to predict the metabolic cluster of cows during early lactation from the milk composition. All the models were evaluated using a leave-group-out cross-validation, meaning data from a single cow were always present in either train or test data to avoid any data leakage. Features were either milk fatty acids (MFA) determined by gas chromatography (MFA [GC]) or features that could be determined during a routine dairy herd improvement (DHI) analysis, such as concentration of fat, protein, lactose, fat/protein ratio, urea, and somatic cell count (determined and reported routinely in DHI registrations), either or not in combination with MFA and BHB determined by mid-infrared (MIR), denoted as MFA [MIR] and BHB [MIR], respectively, which are routinely analyzed but not routinely reported in DHI registrations yet. Models solely based on fat, protein, lactose, fat/protein ratio, urea and somatic cell count (i.e., DHI model) were characterized by the lowest predictive performance [area under the receiver operating characteristic curve (AUCROC) = 0.69]. The combination of the features of the DHI model with BHB [MIR] and MFA [MIR] powerfully increased the predictive performance (AUCROC = 0.81). The model based on the detailed MFA profile determined by GC analysis did not outperform (AUCROC = 0.81) the model using the DHI-features in combination with BHB [MIR] and MFA [MIR]. Predictions solely based on samples at d 3 were characterized by lower performance (AUCROC DHI + BHB [MIR] + MFA [MIR] model at d 3: 0.75; AUCROC MFA [GC] model at d 3: 0.73). High predictive performance was found using samples from d 9 and 21. To conclude, overall, the DHI + BHB [MIR] + MFA [MIR] model allowed to predict metabolic status during early lactation. Accordingly, these parameters show potential for routine prediction of metabolic status. |
| first_indexed | 2024-04-11T05:53:42Z |
| format | Article |
| id | doaj.art-df82db1c7e5943089252b096911ab4b6 |
| institution | Directory Open Access Journal |
| issn | 0022-0302 |
| language | English |
| last_indexed | 2024-04-11T05:53:42Z |
| publishDate | 2023-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of Dairy Science |
| spelling | doaj.art-df82db1c7e5943089252b096911ab4b62022-12-22T04:41:58ZengElsevierJournal of Dairy Science0022-03022023-01-011061690702Diagnostic milk biomarkers for predicting the metabolic health status of dairy cattle during early lactationS. Heirbaut0X.P. Jing1B. Stefańska2E. Pruszyńska-Oszmałek3L. Buysse4P. Lutakome5M.Q. Zhang6M. Thys7L. Vandaele8V. Fievez9Laboratory for Animal Nutrition and Animal Product Quality, Department of Animal Sciences and Aquatic Ecology, Faculty of Bioscience Engineering, Ghent University, 9000 Gent, BelgiumLaboratory for Animal Nutrition and Animal Product Quality, Department of Animal Sciences and Aquatic Ecology, Faculty of Bioscience Engineering, Ghent University, 9000 Gent, Belgium; State Key Laboratory of Grassland and Agro-Ecosystems, College of Pastoral Agriculture Science and Technology, Lanzhou University, Lanzhou 730020, ChinaDepartment of Grassland and Natural Landscape Sciences, Poznań University of Life Sciences, Dojazd 11 Street, 60-632 Poznań, PolandDepartment of Animal Physiology, Biochemistry, and Biostructure, Poznań University of Life Sciences, Wołyńska 35 Street, 60-637 Poznań, PolandLaboratory for Animal Nutrition and Animal Product Quality, Department of Animal Sciences and Aquatic Ecology, Faculty of Bioscience Engineering, Ghent University, 9000 Gent, BelgiumLaboratory for Animal Nutrition and Animal Product Quality, Department of Animal Sciences and Aquatic Ecology, Faculty of Bioscience Engineering, Ghent University, 9000 Gent, Belgium; School of Agricultural and Environmental Sciences, Mountains of the Moon University, PO Box 837, Fort Portal, Uganda; Department of Agricultural Production, College of Agricultural and Environmental Sciences, Makerere University, PO Box 7062, Kampala, UgandaLaboratory for Animal Nutrition and Animal Product Quality, Department of Animal Sciences and Aquatic Ecology, Faculty of Bioscience Engineering, Ghent University, 9000 Gent, BelgiumILVO, Scheldeweg 68, 9090 Melle, BelgiumILVO, Scheldeweg 68, 9090 Melle, BelgiumLaboratory for Animal Nutrition and Animal Product Quality, Department of Animal Sciences and Aquatic Ecology, Faculty of Bioscience Engineering, Ghent University, 9000 Gent, Belgium; Corresponding authorABSTRACT: Data on metabolic profiles of blood sampled at d 3, 6, 9, and 21 in lactation from 117 lactations (99 cows) were used for unsupervised k-means clustering. Blood metabolic parameters included β-hydroxybutyrate (BHB), nonesterified fatty acids, glucose, insulin-like growth factor-1 (IGF-1) and insulin. Clustering relied on the average and range of the 5 blood parameters of all 4 sampling days. The clusters were labeled as imbalanced (n = 42) and balanced (n = 72) metabolic status based on the values of the blood parameters. Various random forest models were built to predict the metabolic cluster of cows during early lactation from the milk composition. All the models were evaluated using a leave-group-out cross-validation, meaning data from a single cow were always present in either train or test data to avoid any data leakage. Features were either milk fatty acids (MFA) determined by gas chromatography (MFA [GC]) or features that could be determined during a routine dairy herd improvement (DHI) analysis, such as concentration of fat, protein, lactose, fat/protein ratio, urea, and somatic cell count (determined and reported routinely in DHI registrations), either or not in combination with MFA and BHB determined by mid-infrared (MIR), denoted as MFA [MIR] and BHB [MIR], respectively, which are routinely analyzed but not routinely reported in DHI registrations yet. Models solely based on fat, protein, lactose, fat/protein ratio, urea and somatic cell count (i.e., DHI model) were characterized by the lowest predictive performance [area under the receiver operating characteristic curve (AUCROC) = 0.69]. The combination of the features of the DHI model with BHB [MIR] and MFA [MIR] powerfully increased the predictive performance (AUCROC = 0.81). The model based on the detailed MFA profile determined by GC analysis did not outperform (AUCROC = 0.81) the model using the DHI-features in combination with BHB [MIR] and MFA [MIR]. Predictions solely based on samples at d 3 were characterized by lower performance (AUCROC DHI + BHB [MIR] + MFA [MIR] model at d 3: 0.75; AUCROC MFA [GC] model at d 3: 0.73). High predictive performance was found using samples from d 9 and 21. To conclude, overall, the DHI + BHB [MIR] + MFA [MIR] model allowed to predict metabolic status during early lactation. Accordingly, these parameters show potential for routine prediction of metabolic status.http://www.sciencedirect.com/science/article/pii/S0022030222006452metabolic statusmilk compositionpredictive modelingdairy cattle |
| spellingShingle | S. Heirbaut X.P. Jing B. Stefańska E. Pruszyńska-Oszmałek L. Buysse P. Lutakome M.Q. Zhang M. Thys L. Vandaele V. Fievez Diagnostic milk biomarkers for predicting the metabolic health status of dairy cattle during early lactation Journal of Dairy Science metabolic status milk composition predictive modeling dairy cattle |
| title | Diagnostic milk biomarkers for predicting the metabolic health status of dairy cattle during early lactation |
| title_full | Diagnostic milk biomarkers for predicting the metabolic health status of dairy cattle during early lactation |
| title_fullStr | Diagnostic milk biomarkers for predicting the metabolic health status of dairy cattle during early lactation |
| title_full_unstemmed | Diagnostic milk biomarkers for predicting the metabolic health status of dairy cattle during early lactation |
| title_short | Diagnostic milk biomarkers for predicting the metabolic health status of dairy cattle during early lactation |
| title_sort | diagnostic milk biomarkers for predicting the metabolic health status of dairy cattle during early lactation |
| topic | metabolic status milk composition predictive modeling dairy cattle |
| url | http://www.sciencedirect.com/science/article/pii/S0022030222006452 |
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