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Predicting lameness in dairy cattle using untargeted liquid chromatography–mass spectrometry-based metabolomics and machine learning

Lameness in dairy cattle is a highly prevalent condition that impacts on the health and welfare of dairy cows. Prompt detection and implementation of effective treatment is important for managing lameness. However, major limitations are associated with visual assessment of lameness, which is the mos...

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Autores principales: Randall, Laura V., Kim, Dong-Hyun, Abdelrazig, Salah M.A., Bollard, Nicola J., Hemingway-Arnold, Heather, Hyde, Robert M., Thompson, Jake S., Green, Martin J.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: American Dairy Science Association 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10570404/
https://www.ncbi.nlm.nih.gov/pubmed/37500436
http://dx.doi.org/10.3168/jds.2022-23118
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author Randall, Laura V.
Kim, Dong-Hyun
Abdelrazig, Salah M.A.
Bollard, Nicola J.
Hemingway-Arnold, Heather
Hyde, Robert M.
Thompson, Jake S.
Green, Martin J.
author_facet Randall, Laura V.
Kim, Dong-Hyun
Abdelrazig, Salah M.A.
Bollard, Nicola J.
Hemingway-Arnold, Heather
Hyde, Robert M.
Thompson, Jake S.
Green, Martin J.
author_sort Randall, Laura V.
collection PubMed
description Lameness in dairy cattle is a highly prevalent condition that impacts on the health and welfare of dairy cows. Prompt detection and implementation of effective treatment is important for managing lameness. However, major limitations are associated with visual assessment of lameness, which is the most commonly used method to detect lameness. The aims of this study were to investigate the use of metabolomics and machine learning to develop novel methods to detect lameness. Untargeted metabolomics using liquid chromatography-mass spectrometry (LC-MS) alongside machine learning models and a stability selection method were utilized to evaluate the predictive accuracy of differences in the metabolomics profile of first-lactation dairy cows before (during the transition period) and at the time of lameness (based on visual assessment using the 0–3 scale of the Agriculture and Horticulture Development Board). Urine samples were collected from 2 cohorts of dairy heifers and stored at −86°C before analysis using LC-MS. Cohort 1 (n = 90) cows were recruited as current first-lactation cows with weekly mobility scores recorded over a 4-mo timeframe, from which newly lame and nonlame cows were identified. Cohort 2 (n = 30) cows were recruited within 3 wk before calving, and lameness events (based on mobility score) were recorded through lactation until a minimum of 70 d in milk (DIM). All cows were matched paired by DIM ± 14 d. The median DIM at lameness identification was 187.5 and 28.5 for cohort 1 and 2, respectively. The best performing machine learning models predicted lameness at the time of lameness with an accuracy of between 81 and 82%. Using stability selection, the prediction accuracy at the time of lameness was 80 to 81%. For samples collected before and after calving, the best performing machine learning model predicted lameness with an accuracy of 71 and 75%, respectively. The findings from this study demonstrate that untargeted LC-MS profiling combined with machine learning methods can be used to predict lameness as early as before calving and before observable changes in gait in first-lactation dairy cows. The methods also provide accuracies for detecting lameness at the time of observable changes in gait of up to 82%. The findings demonstrate that these methods could provide substantial advancements in the early prediction and prevention of lameness risk. Further external validation work is required to confirm these findings are generalizable; however, this study provides the basis from which future work can be conducted.
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spelling pubmed-105704042023-10-14 Predicting lameness in dairy cattle using untargeted liquid chromatography–mass spectrometry-based metabolomics and machine learning Randall, Laura V. Kim, Dong-Hyun Abdelrazig, Salah M.A. Bollard, Nicola J. Hemingway-Arnold, Heather Hyde, Robert M. Thompson, Jake S. Green, Martin J. J Dairy Sci Research Lameness in dairy cattle is a highly prevalent condition that impacts on the health and welfare of dairy cows. Prompt detection and implementation of effective treatment is important for managing lameness. However, major limitations are associated with visual assessment of lameness, which is the most commonly used method to detect lameness. The aims of this study were to investigate the use of metabolomics and machine learning to develop novel methods to detect lameness. Untargeted metabolomics using liquid chromatography-mass spectrometry (LC-MS) alongside machine learning models and a stability selection method were utilized to evaluate the predictive accuracy of differences in the metabolomics profile of first-lactation dairy cows before (during the transition period) and at the time of lameness (based on visual assessment using the 0–3 scale of the Agriculture and Horticulture Development Board). Urine samples were collected from 2 cohorts of dairy heifers and stored at −86°C before analysis using LC-MS. Cohort 1 (n = 90) cows were recruited as current first-lactation cows with weekly mobility scores recorded over a 4-mo timeframe, from which newly lame and nonlame cows were identified. Cohort 2 (n = 30) cows were recruited within 3 wk before calving, and lameness events (based on mobility score) were recorded through lactation until a minimum of 70 d in milk (DIM). All cows were matched paired by DIM ± 14 d. The median DIM at lameness identification was 187.5 and 28.5 for cohort 1 and 2, respectively. The best performing machine learning models predicted lameness at the time of lameness with an accuracy of between 81 and 82%. Using stability selection, the prediction accuracy at the time of lameness was 80 to 81%. For samples collected before and after calving, the best performing machine learning model predicted lameness with an accuracy of 71 and 75%, respectively. The findings from this study demonstrate that untargeted LC-MS profiling combined with machine learning methods can be used to predict lameness as early as before calving and before observable changes in gait in first-lactation dairy cows. The methods also provide accuracies for detecting lameness at the time of observable changes in gait of up to 82%. The findings demonstrate that these methods could provide substantial advancements in the early prediction and prevention of lameness risk. Further external validation work is required to confirm these findings are generalizable; however, this study provides the basis from which future work can be conducted. American Dairy Science Association 2023-10 /pmc/articles/PMC10570404/ /pubmed/37500436 http://dx.doi.org/10.3168/jds.2022-23118 Text en © 2023 The Authors. https://creativecommons.org/licenses/by/4.0/This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Research
Randall, Laura V.
Kim, Dong-Hyun
Abdelrazig, Salah M.A.
Bollard, Nicola J.
Hemingway-Arnold, Heather
Hyde, Robert M.
Thompson, Jake S.
Green, Martin J.
Predicting lameness in dairy cattle using untargeted liquid chromatography–mass spectrometry-based metabolomics and machine learning
title Predicting lameness in dairy cattle using untargeted liquid chromatography–mass spectrometry-based metabolomics and machine learning
title_full Predicting lameness in dairy cattle using untargeted liquid chromatography–mass spectrometry-based metabolomics and machine learning
title_fullStr Predicting lameness in dairy cattle using untargeted liquid chromatography–mass spectrometry-based metabolomics and machine learning
title_full_unstemmed Predicting lameness in dairy cattle using untargeted liquid chromatography–mass spectrometry-based metabolomics and machine learning
title_short Predicting lameness in dairy cattle using untargeted liquid chromatography–mass spectrometry-based metabolomics and machine learning
title_sort predicting lameness in dairy cattle using untargeted liquid chromatography–mass spectrometry-based metabolomics and machine learning
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10570404/
https://www.ncbi.nlm.nih.gov/pubmed/37500436
http://dx.doi.org/10.3168/jds.2022-23118
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