Both blood- and milk-based biomarkers have been analysed for decades in research settings, although often only in one herd, and without focus on the variation in the biomarkers that are specifically related to herd or diet. Biomarkers can be used to detect physiological imbalance and disease risk and may have a role in precision livestock farming (PLF). For use in PLF, it is important to quantify normal variation in specific biomarkers and the source of this variation. The objective of this study was to estimate the between- and within-herd variation in a number of blood metabolites (β-hydroxybutyrate (BHB), non-esterified fatty acids, glucose and serum IGF-1), milk metabolites (free glucose, glucose-6-phosphate, urea, isocitrate, BHB and uric acid), milk enzymes (lactate dehydrogenase and N-acetyl-β-D-glucosaminidase (NAGase)) and composite indicators for metabolic imbalances (Physiological Imbalance-index and energy balance), to help facilitate their adoption within PLF. Blood and milk were sampled from 234 Holstein dairy cows from 6 experimental herds, each in a different European country, and offered a total of 10 different diets. Blood was sampled on 2 occasions at approximately 14 days-in-milk (DIM) and 35 DIM. Milk samples were collected twice weekly (in total 2750 samples) from DIM 1 to 50. Multilevel random regression models were used to estimate the variance components and to calculate the intraclass correlations (ICCs). The ICCs for the milk metabolites, when adjusted for parity and DIM at sampling, demonstrated that between 12% (glucose-6-phosphate) and 46% (urea) of the variation in the metabolites’ levels could be associated with the herd-diet combination. Intraclass Correlations related to the herd-diet combination were generally higher for blood metabolites, from 17% (cholesterol) to approximately 46% (BHB and urea). The high ICCs for urea suggest that this biomarker can be used for monitoring on herd level. The low variance within cow for NAGase indicates that few samples would be needed to describe the status and potentially a general reference value could be used. The low ICC for most of the biomarkers and larger within cow variation emphasises that multiple samples would be needed - most likely on the individual cows - for making the biomarkers useful for monitoring. The majority of biomarkers were influenced by parity and DIM which indicate that these should be accounted for if the biomarker should be used for monitoring.

The large increase in milk yield and the structural changes in the dairy industry have caused major changes in the housing, feeding and management of the dairy cow. However, while large improvements have occurred in production and efficiency, the disease incidence, based on veterinary records, does not seem to be improved. Earlier reviews have covered critical periods such as the transition period in the cow and its influence on health and immune function, the interplay between the endocrine system and the immune system and nutrition and immune function. Knowledge on these topics is crucial for our understanding of disease risk and our effort to develop health and welfare improving strategies, including proactive management for preventing diseases and reducing the severity of diseases. To build onto this the main purpose of this review will therefore be on the effect of physiological imbalance (PI) on immune function, and to give perspectives for prevention of diseases in the dairy cow through nutrition. To a large extent, the health problems during the periparturient period relate to cows having difficulty in adapting to the nutrient needs for lactation. This may result in PI, a situation where the regulatory mechanisms are insufficient for the animals to function optimally leading to a high risk of a complex of digestive, metabolic and infectious problems. The risk of infectious diseases will be increased if the immune competence is reduced. Nutrition plays a pivotal role in the immune response and the effect of nutrition may be directly through nutrients or indirectly by metabolites, for example, in situations with PI. This review discusses the complex relationships between metabolic status and immune function and how these complex interactions increase the risk of disease during early lactation. A special focus will be placed on the major energetic fuels currently known to be used by immune cells (i.e. glucose, non-esterified fatty acids, beta-hydroxybutyrate and glutamine) and how certain metabolic states, such as degree of negative energy balance and risk of PI, contribute to immunosuppression during the periparturient period. Finally, we will address some issues on disease prevention through nutrition.