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• W2996765502 abstract "In early lactation, dairy cows typically experience a negative energy balance (NEB) due to the high energy requirement for milk yield and low energy intake from feed. Negative energy balance has been related to metabolic disorders, compromised health and fertility, and reduced productive lifespan. Estimation of the energy balance and metabolic status is not easy on the farm. Therefore, the aims of this thesis were to estimate energy balance and metabolic status of dairy cows using metabolomics and machine learning techniques, and to investigate the metabolic pathways related to energy metabolism of dairy cows in early lactation using metabolomics and proteomics techniques. In this thesis, on-farm cow data collected from two earlier studies (study I, 168 cows and study II, 127 cows) were used to estimate energy balance and metabolic status of cows with machine learning approach. In addition, milk and blood samples obtained from study II were analysed with metabolomics and proteomics approaches. To estimate energy balance of dairy cows, estimated performance of reduced models with either milk metabolites, or milk production traits or both ranged from 0.53 to 0.78 (adjusted R-square).Milk metabolites important in explaing negative energy balance in cows where glycine, choline and carnitine. . To estimate metabolic status of dairy cows using on-farm cow data, random forest, support vector machine, and partial least square discriminant analysis performed better than other machine learning algorithms. Based on the metabolomics results, plasma and milk metabolites altered during NEB of dairy cows in early lactation reflected the metabolism in the body or the mammary gland of dairy cows. Metabolic processes in the mammary gland during NEB were related to leakage of cell content due to mammary cell apoptosis and, to synthesis of nucleic acids and cell membrane phospholipids, protein glycosylation and an increase in one-carbon metabolic processes. The processes are related to cell renewal and proliferation. Since NEB is highly related to milk production this seem logical. Blood metabolites related to energy balance were mainly reflecting energy metabolism (mobilization of body fat, skeleton muscle, bone) increased blood flow and gluconeogenesis. Better understanding of the metabolic pathways through a metabolomics and proteomics approach does not only provide biomarkers for pathways under stress during NEB but may also allow for targeted dietary interventions when glucose and rumen protected choline are interesting candidates. In conclusion, the energy balance of dairy cows can be estimated by milk metabolites based on metabolomics study, and metabolic status can be estimated by machine learning algorithms using on-farm cow data. Moreover, energy balance of dairy cows in early lactation was related with milk and plasma metabolites which revealed metabolic pathways that allow more targeted intervention strategies." @default.
• W2996765502 created "2020-01-10" @default.
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• W2996765502 date "2019-12-11" @default.
• W2996765502 modified "2023-09-25" @default.
• W2996765502 title "Energy balance and metabolic status of dairy cows : a study using metabolomics, proteomics and machine learning approaches" @default.
• W2996765502 doi "https://doi.org/10.18174/500864" @default.
• W2996765502 hasPublicationYear "2019" @default.
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