Award details

Quantitative metabolomics for prediction of lameness and elucidation of related mechanistic pathways in dairy cattle

Reference	BB/W005654/1
Principal Investigator / Supervisor	Dr Laura Randall
Co-Investigators / Co-Supervisors
Institution	University of Nottingham
Department	School of Veterinary Medicine and Sci
Funding type	Research
Value (£)	671,474
Status	Current
Type	Research Grant
Start date	01/09/2022
End date	31/08/2026
Duration	48 months

Abstract

The combination of machine learning and untargeted metabolomics offers a key opportunity for biomarker discovery and gaining a deep insight into disease processes. Metabolomics in itself offers an immensely powerful tool that has come to the forefront as a technique for providing insights into pathophysiological processes and early disease diagnostics. When applied to complex diseases, significant advancements in understanding disease and disease risk can be achieved, that have not previously been possible. These techniques have been vastly underutilised in animal health and in particular for the early prediction of disease. Our pilot work has recently demonstrated the value of utilising a combination of untargeted (not restricted to a sub-set of a priori chosen metabolites or pathways) metabolomics with machine learning to develop algorithms to accurately predict one of the most important complex diseases of livestock; lameness. Lameness in dairy cattle is a painful and debilitating condition with profound health, welfare and economic impacts as well as broader impacts on sustainability from reduced efficiencies. Yet major gaps in exist in our understanding of the disease and diagnostics are limited to measurement of gait changes when pain is evident. This project will utilise the novel application of metabolomics and machine learning to develop algorithms to predict disease risk during first lactation at an early stage (within 2 weeks of calving and 2-6 weeks prior to lameness) and identify key biomarkers for lameness. In turn, these will be used to understand mechanistic pathways that contribute to lameness, providing unique insights into pathophysiology. The value of this information for the livestock industry should not be underestimated; prediction of economically important conditions, such as lameness, would be an exceptionally valuable management tool and this approach could be used for many other complex conditions.

Summary

Complex diseases of humans and livestock are common and extremely challenging to manage. Many factors contribute towards an individual's risk of experiencing disease, including genetic and non-genetic factors. When trying to manage disease risk, the relative importance of these different factors needs to be measured and understood, which is difficult to achieve. Ultimately, these challenges often result in a lack of knowledge and understanding of how to prevent or reduce disease risk in a population. Lameness (impaired mobility) is one of the highest priority diseases for the UK dairy industry. This painful and debilitating condition affects cattle welfare as well as production and health. Current estimates suggest that at any one time ~30% of dairy cows in UK herds are suffering from lameness. Implications extend beyond the cow to the sustainability of dairy farming and environmental impacts from disease reducing efficiencies. As a complex disease with many factors contributing to its occurrence, lameness is inherently challenging to tackle and major gaps exist in our understanding of disease processes contributing to lameness. What is known, is that cows entering their first lactation (heifers) are the most important group of cattle in a dairy herd in terms of preventing lameness; heifers represent the future of the herd and once cows experience a first episode of lameness pathological changes occur in the foot that place them at higher risk of recurrence. If we are to prevent lameness occurring in the first place, attention should be focussed on heifers. Detection of lameness is currently only possible at an advanced stage of disease when pain causes cows to walk with an altered gait. The ability to detect lameness early on, prior to this stage, would be a huge advancement on the current situation resulting in improved health, welfare and productivity. Metabolomics is a technique that allows the end products (metabolome) of genetic and non-genetic factors influencing disease risk to be measured. By comparing the metabolome of diseased and healthy individuals, signatures (biomarkers) of disease, can be identified. To make these comparisons, complex statistical methods can be used; a powerful combination of statistics and metabolomics can identify disease biomarkers and further our understanding of processes contributing to disease risk. Our previous work has shown that by using this approach lameness can be predicted with an accuracy of 93% from the metabolome of urine samples collected prior to calving (1 - 10 weeks prior to lameness). This project, will use an innovative combination of metabolomics and artificial intelligence to identify biomarkers for lameness in dairy cows. Groups of 160 dairy heifers will be monitored over a prolonged period of time (up to 305 days or one lactation per animal) with collection of regular urine, plasma and milk samples and data related to lameness, health and production; providing a valuable resource for this and future projects. Paired samples will be selected from lame and non-lame first lactation cows shortly before the first case of lameness and prior to calving. These time-points have been selected as being important in the development of lesions causing lameness. Computational models will be developed to predict the occurrence of lameness by comparing the metabolome of diseased and healthy individuals; identifying signals in the metabolome that predict lameness and using these to understand the disease processes occurring. Results will provide a vital insight into disease pathways contributing to lameness in dairy cows and the ability to predict disease risk. This in turn will inform disease management and provide a tool for early prediction of lameness, offering a step change in the detection and management of lameness in dairy cows. This approach is applicable to many other complex diseases in both livestock and humans and will be further utilised in future work.

Committee	Research Committee A (Animal disease, health and welfare)
Research Topics	Animal Health
Research Priority	X – Research Priority information not available
Research Initiative	X - not in an Initiative
Funding Scheme	X – not Funded via a specific Funding Scheme

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